Dissertations Completed With ITS Support, 1992-2018
Among the main freight modeling approaches, commodity-based models stand out in their ability to incorporate all travel modes and capture the economic mechanisms driving freight movements. However, challenges still exist on the effective use of public freight data and the ability to accurately reflect the supply chain relationships between commodities. In this research, a commodity-based framework for freight demand forecasting using a Structural Regression Model (SRM) is explored, and applied to the original California Statewide Freight Forecasting Model (CSFFM) using the Freight Analysis Framework Version 4 (FAF4) data.
The framework developed in this study contains four innovative components: (1) mathematical approach for determining freight economic centroids; (2) the aggregation of commodities using the Fuzzy C-means clustering algorithm; (3) employing weighted travel distance by commodity group (CG) instead of highway skim to provide a more representative travel distance across multiple modes; and (4) the forecasting of freight demand using SRM method to comprehensively consider the direct effect, indirect effect and latent variables. The SRM is adopted in both the total generation model and domestic direct demand model. The
application results are further compared with the original CSFFM forecasts in 2012 to illustrate
the advantages of the proposed framework.
Transportation systems have been traditionally operated on a First-Come-First-Served (FCFS) fashion. FCFS consumption of supply naturally arises from supply being centralized by an infrastructure operator, without considering any individual-specific information from users. Thus, FCFS behaves as a status quo policy, generally considered as fair, since it is acknowledged that all users are treated equally. We know though, that there exists heterogeneity in users’ value of time and delay savings. Taking advantage of smartphones and connected vehicle environments, it is now possible to include this user heterogeneity into operations in order to increase overall system efficiency and fairness. I call this novel operational paradigm, collaborative consumption of transportation supply.
This seminar explores the idea of violating FCFS by allowing users to trade in real-time the part of supply they “own” while they participate into a transportation system. This de facto ownership emanates from the space-time region which each agent lawfully controls. This topic led me to introduce by the first time in transportation literature, the fundamental economic concept of envy-freeness as a behavioral paradigm. I also have expanded this concept to the domain of dynamic problems, which I call dynamic envy-freeness, and created a new envy-minimizing criteria family, which strongly fits into the existing axiomatic body of Welfare Economics.
Several applications of collaborative consumption are explored in this dissertation. First, I create PEXIC, Priced EXchanges in Intersection Control, in which users can pay other vehicles to reduce their delays in a fair manner. Second, Peer-to-peer (P2P) ride exchange in ridesharing systems, where trip property rights are transferred to users in such a way that they can trade their rides between each other. Third, I have studied a new operational policy in highway control: queue-jumping operations, where vehicles can jump a queue by paying the overtaken vehicles such that the resulting queue is fair and stable. Fourth, I have modelled a P2P ridesharing system as a truthful dual-role market exchange economy, which guarantees a ride-back. Finally, I present how collaborative consumption will be extended to urban systems.
This three-essay dissertation focuses on understanding linkages between urban form, travel behavior, ownership of alternative fuel vehicles, active commuting, congestion, fuel consumption, and air pollution (including greenhouse gas emissions). These essays estimated different specifications of Generalized Structural Equation Models (GSEM) to explicitly account for residential self-selection and vehicle choice endogeneities.
The first essay analyzes the influence of land use policies and gasoline prices on driving patterns. I estimated a Generalized Structural Equation Model (GSEM) with a Tobit-link specification on a Southern California subsample of the 2009 National Household Travel Survey (NHTS). These data haves a quasi-experimental nature thanks to large exogenous variation in gasoline price during the survey period. I analyzed separately home-based work trips and non-work trips under the hypothesis that households have more flexibility to adjust their non-work trips when gasoline prices change, whereas most of the literature does not take trip purpose into account. To measure urban form, which is treated as a latent construct, I used fine-grained geospatial information including population density, land use mix, employment density, distance to employment centers and transit availability. I found that, in the short run, households drive 0.171% less for non-work trips when gasoline prices increase by 1%, while work trips are not responsive to gasoline price changes. This suggests that, in the short term, higher fuel prices reduce discretionary driving such as shopping and recreational trips, but they do not affect non-discretionary driving such as commuting trips. My results also suggest that policies that seek to increase transit service and housing opportunities near employment centers will reduce driving.
The second essay investigates the impact of government incentives such as access exemption to High Occupancy Vehicle (HOV) lanes and parking privileges on household ownership of Alternative Fuel Vehicles (AFVs) using Generalized Structural Equation Models (GSEM), and accounts for residential self-selection, household demographics and ambient political-environmentalism. I analyzed geocoded travel diary data from the 2012 California Household Travel Survey (CHTS), linked with fueling station data from the US Department of Energy Alternative Fuels Data Center and precinct level election data from the UC Berkeley Statewide Database. My findings suggest that, on average, households with alternative fuel vehicles drive approximately 10 miles more on weekdays and about 0.5 miles more on non-discretionary trips than otherwise similar households. In addition, households who live closer to a freeway with HOV lanes, work closer to an AFV charging facility (that provides free parking), and are likely supportive of pro-environmental measures are more likely to own alternative fuel vehicles.
The third essay examines the influence of urban form on transit use and non-motorized travel (NMT, including biking and walking) for households (with at least one employed adult) in Los Angeles and Orange Counties in California based on 2009 National Household Travel Survey (NHTS) data. The objectives of the research are (1) to assess several methods for measuring urban form features in the near-residence and near-workplace environments and (2) to assess the importance of these urban form features on transit use and NMT after accounting for the influence of these features on household vehicle ownership and residential selection. Results provide insights into the relative influence of several specifications of population density, transit access and walkability measures on transit use and NMT for commute and non-work trips. Reduced form models suggest that the dominant determinant of discretionary travel is household socio-demographic status. In terms of residential selection, lower income, younger, and smaller households are more likely to choose a dense, pedestrian friendly, and transit rich neighborhood. In terms of vehicle ownership, households living in high density, pedestrian friendly, and transit rich neighborhoods are less likely to own vehicles. After accounting for the influence of urban form on vehicle ownership and residential selection, workplace transit accessibility has greater influence on transit commuting than transit access near a household’s residence. Results vary by how urban form is specified and by the source of travel data. Finally, there is some evidence that population density affects active travel for discretionary purposes.
More frequent vehicle movements are required for moving containers in a local area due to low unit volume that a single vehicle can handle compared with vessels and rails involved in the container supply chain. For this reason, truck operations for moving containers significantly affect not only transportation cost itself but also product price. They have inherent operational inefficiencies associated with empty container movements and container processes at facilities such as warehouses, distribution centers and intermodal terminals. One critical issue facing the trucking industry is the pressing need for truck routing plans that reduce such inefficiencies. Hence, this dissertation proposes to apply the concept of sharing resources, which is an emerging economic model, to container truck operations in order to resolve this issue. Two shareable resources – vehicles and containers – are considered.
This study extends the literature on routing and scheduling problems that arise from container movements, and examines the possible benefits of sharing resources across customers. A series of truck container routing and scheduling problems were developed by assuming different levels of resource sharing among; (1) customers of one trucking operator, (2) customers across collaborations of multiple operators, and (3) customers over multi-day operations. To enable a trucking company to operate its fleet under a shared resource environment, two operational strategies – street turning and decoupling operations – together with temporal precedence constraints – in addition to the time constraints that are typically included in the vehicle routing problem with time windows (VRPTW) – were adopted to address the proposed problems.
Two meta-heuristic algorithms based on a variable neighborhood search (VNS) scheme were developed to solve the proposed problems, including temporal precedence constraints – which are computationally more expensive – for real-world applications. To address flexible time windows resulting from temporal precedence constraints, a novel feasibility check algorithm was developed.
Results from a series of numerical experiments confirm that the proposed approach leverages the advantages of resource sharing, and the meta-heuristic algorithms are efficient solution approaches for each problem with the targeted resource sharing. Consequently, this dissertation offers a platform for the development of a decision-support tool for drayage companies by applying three different levels of resource sharing into their operations.
Wan-Tzu Lo Compact development and gender inequality: do more accessible and walkable built environments promote gender equality in travel and activity space behaviors? PhD, Transportation Science, 2017, Adviser: Douglas Houston
Researchers have been concerned that suburban sprawl could reinforce gendered mobility patterns and lead to gendered differences in mobility. Previous studies also argued that the effectiveness of land use policy could be influenced by men and women’s different mobility patterns in response to built environments. To address these concerns, this dissertation uses the 2010-2012 California Household Travel Survey data and directly compares the within-household gendered travel and spatial behaviors for households with paired heads living in Southern California. The study examines whether built environments, including destination accessibility, design and walkability have different impacts on male and female heads’ daily travel and activity space behaviors and whether potential urban design can help improve gendered inequality in daily mobility.
Based on negative binomial, Tobit, and generalized least squares regressions, the results show that that male and female heads respond to built environments with different travel and spatial behaviors. Living in walkable and accessible areas is likely to encourage male heads to walk, reduce their dependence on driving, locate activity center close to home, and have spatially concentrated activities. Female heads tend to respond to walkable and accessible living environments with reducing automobile travel and with centering and confining their activities near residential neighborhoods. The negative binomial, Tobit, and binary logit regression analyses that investigate the influences of built environments on gendered inequality indicate that high walkability and regional accessibility are likely to reduce the gendered inequality in motorized travel distance and relax female heads’ spatial (and temporal) constraints relative to their husbands.
This dissertation contributes to the policy debates by informing planners and feminist geographers that the effects of built environments can be heterogeneous even for men and women from similar backgrounds and compact design can be the key to gendered equity. Given that compact developments are being rapidly implemented in Southern California, this dissertation study is expected to help shape effective and efficient land use policies in the future.
An efficient transportation system requires adequate and well-maintained infrastructure to relieve congestion, reduce accidents, and promote economic competitiveness. However, there is a growing gap between public financial commitments and the cost of maintaining, let alone expanding, the U.S. road transportation infrastructure. Moreover, the tools used to evaluate transportation infrastructure investments are typically deterministic and rely on present value calculations, even though it is well-known that this approach is likely to result in sub-optimal decisions in the presence of uncertainty, which is pervasive in transportation infrastructure decisions. In this context, the purpose of this dissertation is to propose a framework based on real options and advanced numerical methods to make better road infrastructure decisions in the presence of demand uncertainty.
I first develop a real option framework to find the optimal investment timing, endogenous toll rate, and road capacity of a private inter-city highway under demand uncertainty. Traffic congestion is represented by a BPR function, competition with an existing road is captured by user equilibrium, and travel demand between the two cities follows a geometric Brownian motion with a reflecting upper barrier. I derive semi-analytical solutions for the investment threshold, the dynamic toll rates and the optimum capacity. Result shows the importance of modeling congestion and an upper demand barrier – features that are missing from previous studies.
I then extend this real options framework to study two additional ways of funding an inter-city highway project: with public funds or via a Public-Private Partnership (PPP). Using Monte Carlo simulation, I investigate the value of a non-compete clause for both a local government and for private firms involved in the PPP.
Since road infrastructure investments are rarely made in isolation, I also extend my real options framework to the Multi-period Continuous Network Design Problem (CNDP), to analyze the investment timing and capacity of multiple links under demand uncertainty. No algorithm is currently available to solve the multi-period CNDP under uncertainty in a reasonable time. I propose and test a new algorithm called “Approximate Least Square Monte Carlo simulation” that dramatically reduces the computing time to solve the CNDP while generating accurate solutions.
Trip chaining is a common phenomenon generally known as linking multiple activities and trips in one travel process. A good understanding about trip chaining complexity is important for travel demand model development and for transportation policy design. However, most of the existing studies on trip chaining limit the complexity classification scheme on number of trips chained and neglect other dimensions that also elevate the degree of complexity. The purpose of this study is to develop a new approach, Tour Complexity Index (TCI), that integrates the multi-dimensional nature of trip chaining into the complexity assessment.
TThe study contains three analysis components. The first component introduces the TCI approach as a trip chaining complexity measure that not only considers number of trips chained but also includes the spatial relationship across destinations, the route arrangement, and the urban environment of the destinations. By comparing descriptive statistics and generalized linear model results from TCI approach with those from traditional approach, we find that the TCI approach offers more information regarding trip chaining and mode choice. The application of TCI is further demonstrated in the following components. The second component investigates the intrapersonal daily and weekly travel variability with travel characterized by TCI and mode choice. The result reinforces an argument in current literature that the common single-day travel survey may produce biased estimation due to the day-to-day variance in travel behavior. Result also finds that proximity to a new transit service from place of residence is connected with a decline in variability. The third component explores a framework for travel pattern recognition where pattern is characterized by TCI as well. The discrepancy analysis which is a generalized analysis of variance (ANOVA) method is applied to associate individual characteristics with travel pattern. In addition, both components use Sequential Alignment Method (SAM) for travel pattern representation. The TCI approach and proposed analysis frameworks are validated using the longitudinal GPS trajectory data collected between 2011 and 2013 at west Los Angeles area for Expo Study.
Trucks contribute disproportionally to traffic congestion, emissions, road safety issues, and infrastructure and maintenance costs. In addition, truck flow patterns are known to vary by season and time-of-day as trucks serve different industries and facilities. Therefore, truck flow data are critical for transportation planning, freight modeling, and highway infrastructure design and operations. However, the current data sources only provide partial truck flow or point observations. This dissertation developed a framework for estimating path flows of trucks by tracking individual vehicles as they traverse detector stations over long distances. Truck physical attributes and inductive waveform signatures were collected from advanced point detector systems and used to match vehicles between detector locations by a Selective Weighted Bayesian Model (SWBM). The key feature variables that were the most influential in distinguishing vehicles were identified and emphasized in the SWBM to efficiently and successfully track vehicles across road networks.
The initial results showed that the Bayesian approach with the full integration of two complementary detector data types – advanced inductive loop detectors and Weigh-in-Motion (WIM) sensors – could successfully track trucks over long distances (i.e., 26 miles) by minimizing the impacts of measurement variations and errors from the detection systems. The network implementation of the model demonstrated high coverage and accuracy, which affirmed the capability of the tracking approach to provide comprehensive truck travel patterns in a complex network. Specifically, the model was able to successfully match 90 percent of multi-unit trucks where only 67 percent of trucks observed at a downstream site passed an upstream detection site.
A strategic plan to identify optimal sensor locations to maximize benefits from the truck tracking model was also proposed. A decision model that optimally locates sensors to capture the maximum truck OD and route flow was investigated using a goal programming approach. This approach suggested optimal locations for tracking implementation in a large truck network considering a limited budget. Results showed that sensor locations from a maximum-flow-capturing approach were more advantageous to observe truck flow than a conventional sensor location approach that focuses on OD and route identifiability.
Disasters, specifically earthquakes, result in worldwide catastrophic losses annually. The first seventy-two hours are the most critical and so any reduction in response time is a much-needed contribution. This is especially true in cases where parts of the communication infrastructure are severely damaged. Traditional disaster relief logistics models tend to rely on the assumption that information flow is continuous throughout the system following the onset of a natural disaster. A new integrated framework for disaster relief logistics that optimizes the movement of critical information along with physical movements is proposed in order to alleviate post-disaster conditions in a more accurate and timely manner. The framework consists of an information network and a transportation network with interrelationships. The framework was applied to the Irvine Golden Triangle Network and the Knoxville Network for up to three different cases. The DYNASMART-P simulation program performance was compared against the Time Dependent Network Simplex paths approach combined with the information updating feedback loop. The average total travel times of vehicles travelling to the trauma center in the study areas were compared in order to quantify the improvements of the integrated solution framework. The results show a significant reduction of average total travel times for vehicles transporting injured patients to the trauma center.
Recent advances in communication technology coupled with increasing environmental concerns, road congestion, and the high cost of vehicle ownership have directed more attention to the opportunity cost of empty seats traveling throughout the transportation networks every day. Peer-to-peer (P2P) ridesharing is a good way of using the existing passenger-movement capacity on the vehicles, thereby addressing the concerns about the increasing demand for transportation that is too costly to address via infrastructural expansion.
This dissertation is dedicated to the optimization of the matching process between the participants in a ridesharing system. More specifically, focus of this dissertation is on multi-hop matching, in which riders have the possibility of transferring between vehicles. Different algorithms have been presented for various implementation strategies of ridesharing systems. Multiple case studies assess the important role ridesharing can play as a separate mode, or in conjunction with other modes of transportation, in multi-modal settings.
Modeling capacity is an integral component towards multiple traffic engineering objectives such as design and evaluation of control strategies. Traffic dynamics at bottlenecks, both on freeways and on arterial networks, influenced by bounded acceleration and lane-changing, affect the capacity in intriguing ways. This research attempts to capture these impacts of the bounded acceleration behavior and its interplay with lane-changing, by constructing a modeling framework that accurately models traffic dynamics at bottlenecks.
Towards this goal, first a modified Cell Transmission Model (CTM) is proposed, by substituting the traditionally constant demand function with a linearly decreasing function for congested traffic. The jam-density discharge flow-rate is introduced as an additional parameter to characterize the macroscopic bounded acceleration effects. Analytically the new model is shown to reproduce observed features in the discharge flow-rate and headway at signalized intersections. Calibration with observations from existing studies, as well as new observations, further suggests that the model can reasonably capture all traffic queue discharge features.
The demand function is further modified by integrating macroscopic lane-changing effects on capacity. The Lane Changing Bounded Acceleration CTM (LCBA-CTM) thus developed, is shown to realistically model the capacity drop phenomenon at active freeway lane-drop bottlenecks in stationary states. The capacity drop magnitude is determined by macroscopic bounded acceleration and lane-changing characteristics. Constant loading problems are analytically solved to reveal the onset and recession processes of congestion.
An addition to the framework connects microscopic acceleration profiles of vehicles to modified demand functions. This completes the framework presented by offering a mechanism to start from any acceleration model.
Finally, two applications of the modified CTM are presented illustrating the use of the framework: a) to model impacts of improved vehicle acceleration on traffic dynamics at intersections; and b) to create Macroscopic Fundamental Diagrams (MFDs) for arterial networks and compare their accuracy with traditional CTM methods.
This dissertation offers a systematic approach to incorporating bounded acceleration and lane-changing into the CTM demand functions. Such an approach is shown to capture important static and dynamic features at critical bottlenecks, including lost time and queue discharge features at signalized intersections, as well as capacity drop magnitude and the onset of capacity drop at active freeway bottlenecks. The consistency between the modified demand function and microscopic bounded acceleration models is also established.
During the last two decades, a large body of empirical research has focused on the relationship between land use and travel behavior, and also on the impacts of transportation accessibility on land value. However, significant gaps remain in our understanding of these relationships. In this dissertation, I present three essays on accessibility, carless households, and long-distance travel that will enhance our understandings of relationships among land use, land value, and transportation.
In my first essay, I provide empirical evidence about the magnitude of the value of transportation accessibility as reflected by residential rents in Rajshahi City, Bangladesh. Results of my SARAR (spatial autoregressive model with spatial-autoregressive disturbances) model show a small but statistically significant capitalization of accessibility. Results of this study should be useful for planning transportation infrastructure funding measures in least developed country cities like Rajshahi City.
In my second essay, I assess the joint effects of various socio-economic, life-cycle stage, and land use variables on the likelihood that a household is carless, voluntarily or not, by analyzing data from the 2012 California Household Travel Survey (CHTS). Results of my binary logit models show the importance of land use diversity and of good transit service to help households voluntarily forgo their vehicles, and downplay the impact of population density and pedestrian-friendly facilities. Results of this study should help planners and policy makers formulate policies to curb automobile dependency and help promote sustainable urban transportation.
My third essay analyzes long-distance data from the 2012 CHTS to understand the influence of different socio-economic, land use, and land value variables on the likelihood that a household commutes long-distance in California. Results of my Generalized Structural Equation Model (GSEM) show that long-distance commuting is negatively associated with mixed density and residential home values (around commuters’ residences), but positively related with households’ car-ownership. My results also confirm the presence of residential self-selection. The empirical evidence of this study should help formulate land use planning strategies to curb long-distance commuting and thus help reducing vehicle-miles traveled, which is one way of reducing the emission of greenhouse gases from transportation.
The purpose of this study is to develop a traffic estimation framework which combines different data sources to better reconstruct the traffic states on the freeways. The framework combines both traffic parameters and states estimation in the same work flow, which resolves the inconsistency issue of most existing traffic state estimation methods.
To examine the quality of the traffic sensor data, the study starts with proposing the network sensor health problem (NSHP). The optimal set of sensors is selected from all sensors such that the violation of flow conservation is minimized. The health index for individual detector is then calculated based on the solutions. We also developed a tailored greedy search algorithm to find the solutions effectively. The proposed method is tested using the loop detector data from PeMS on a stretch of the SR-91 freeway. We compared the results with PeMS health status and found considerable level of consistency.
Two different traffic state estimation methods are proposed based on the data availability and traffic states. The LoopReid method is derived from the Newell's simplified kinematic wave model by assuming the whole road segment is fully congested. We formulate a least square optimization problem to find the initial states and traffic parameters based on the first-in-first-out principle and the congested part of the Newell's model. While developing the LoopCT method, we derived a counterpart of the Newell's kinematic wave model in the Lagrangian coordinates under Eulerian boundary conditions. This model also leads to a new method to estimate vehicle trajectories within a road segment. We formulate a least square optimization problem in initial states and traffic parameters which works for mixed traffic states. The two estimation methods turned out to be highly related and the LoopCT method degenerates to the LoopReid method when the traffic is fully congested. The two methods are validated using two datasets from the NGSIM project. Both methods achieved considerable level of accuracy at reconstructing the traffic states and parameters.
Shared-use mobility systems, which enable users to have short-term access to transportation modes on an on-demand basis, have experienced tremendous growth over the last decade. However, most of the existing systems suffer from two co-founding issues: the lack of modeling tools to understand, simulate and predict their behavior and the lack of integration with the existing transit network. To address those issues, this dissertation focuses on investigating the operational challenges of bikesharing systems, with an emphasis on the rebalancing operations and the modeling of a new mobility concept, Car2work, which builds upon existing carsharing ideas and successfully integrates with existing transit networks. A methodological framework to solve the bikesharing rebalancing problem is proposed. The novelties of the approach are that it is proactive instead of reactive, as the bike redistribution occurs before inefficiencies are observed, and uses the outputs of a demand-forecasting technique to decompose the inventory and the routing problem. The decomposition makes the problem scalable, responsive to operator inputs, and able to accommodate user-specific models. Simulation results based on data from the Hubway bikesharing system show that system performance improvements of 7% in the afternoon peak could be achieved.
Car2work main goal is to connect commuters with workplaces while leveraging the line-haul capabilities of existing public transit systems and guaranteeing a trip back home, efficiently tackling the “last mile” problem that is a limiting characteristic of public transit. It differs from the traditional dynamic-ridesharing approaches because it is designed for recurrent commuting trips where commuters announce their (multiple) trips in advanced and an automated all-or-nothing matching strategy is performed, guaranteeing a ride home. The problem is formulated as a pure binary problem that is solved using an aggregation/disaggregation algorithm that renders optimal solutions. The solution approach is based on decomposing the problem into a master problem and a sub-problem, reducing the number of decision variables and constraints. As a result, various instances of the problem can be solved in reasonable amount of time, even when considering the transit network. The model could be used to simulate a large-scale implementation of the concept.
Optimization-based approaches are presented for the design of environmentally oriented road pricing and traffic rationing schemes, particularly with the objective of curbing human exposure to motor vehicle generated air pollutants. The focus on human exposure to pollutants advances previous road pricing and traffic rationing problems which primarily account for congestion minimization, emission minimization, or emissions constraints. Practical utilization of the proposed problems is hindered by their time-consuming nature, so surrogate-based algorithms are developed to accelerate the search for good problem solutions. Given that the algorithms are derivative-free, they can be applied to various types of computationally expensive transportation network design problems.
A toll design problem is proposed for selecting tolling locations and levels that minimize environmental inequality and human exposure to pollutants. A mixed-integer variant of the metric stochastic response surface algorithm and a hybrid genetic algorithm-metric stochastic heuristic are presented to solve the mixed integer toll design problem. Numerical tests suggest that the surrogate-based algorithms have superior performance relative to previous genetic algorithm-based methods.
In addition, an optimization problem is presented for the design of cordon and area-based road pricing schemes subject to environmental constraints. Flexible problem formulations are considered which can be easily utilized with state-of-the-practice transportation planning models. A surrogate-based solution algorithm that uses a geometric representation of the charging area boundary is proposed to solve cordon and area pricing problems.
Lastly, a bi-objective traffic rationing problem is considered where the planner attempts to maximize auto usage while minimizing pollutant exposure inequality, subject to constraints on the levels of greenhouse gas emissions and pollutant concentration levels. A personal pollutant exposure methodology is integrated with standard models used in transportation planning to simulate person-level pollutant intake. To solve this problem a surrogate-assisted differential evolution algorithm for multiobjective continuous optimization problems with constraints is proposed. A sample application illustrates a possible implementation of the traffic rationing problem and the ability of the proposed algorithm to find diverse feasible solutions
Since HFCVs are not yet in the market, there is not enough personal travel data with HFCVs to accurately estimate potential demand. Yet, for fuel companies, sufficient numbers of HFCVs are required before investment in more stations becomes profitable. Alternatively, for customers, sufficient numbers of stations are required before purchasing and operating HFCVs becomes a realistic alternative to ICEVs. So, the initial balancing between this supply and demand confliction is vital to the fate of HFCVs as a market force. This work investigates the effect of refueling availability on choosing HFCVs by finding saturation densities of refueling stations for these vehicles. Using a subsample of households in the NHTS 2009 dataset, we first use parameters of the utility of choosing Toyota Prius vs. Toyota Corolla. We then argue that the values of these coefficients can be transferred to AFVs in general, and used in a preference model for AFVs vs. ICEVs, provided that we also transfer the coefficients of the appropriate purchase and operating costs. Using these models as base, we express the operating costs of AFV with respect to the density of refueling stations and the mean value of time, which then are included in the logit model as variables. We then employ a dynamic normative model that accommodates both the “bandwagon” effect and the results of the estimation of the random utility model of choice to estimate proportions of AFVs in the market over time. Stabilized market proportions are then used for finding saturation densities of stations.
Then, using these results, a competition model is proposed to forecast supplies for HFCVs based on demands forecasted by the dynamic normative model. Feedback models are used connect results derived from the competition and dynamic normative models.
In this research, we propose a series of models designed to take advantage of availability of data—both structured and unstructured—from a variety of sources ranging from passive data, to questionnaires, to social media to analyze underlying patterns and trends of travel and activity behavior. The results support enhancements both in transportation planning and also in the application of programming to support such efforts.
First, a framework for automatically inferring the travel modes and trip purposes of human movement, when tracked by a GPS device, is introduced. We utilize a multiple changepoints algorithm to divide trajectories into segments using only speed data, with no use of referencing information or assumptions about the participants’ temporal or location contexts. Then, Random Forest is used to classify segments into moving and not—moving types. For moving segments, travel mode is predicted. Next, multiple machine learning algorithms are employed, validated, and tested to identify the most suitable model for inferring trip purposes. Estimation results indicate that Random Forest provides the best results. The overall prediction accuracy is over 80% on the testing set—both with and without data on socio-demographic variables—predicting “shop” trips with an accuracy of 92.1%, while its accuracy for “go home” and “studying” trips reaches 100%.
Additionally, we analyze data pertaining to responses to the introduction of light rail service taken in waves to complement and evaluate knowledge about how personal travel behavior varies over time of day, day of week, and between waves. Our results indicate that, although the average of activity duration varies significantly over days of week and waves, the random effect of these two factors on activity duration was minor; time of day contributed over one third of the total variance in the duration.
Finally, the dissertation demonstrates uses of Twitter data as a potentially important data source to understand comments, criticisms, and responses about light rail in Los Angles. This result can be useful for exploring trends among commuters and how their emotions varied according to the light rail line they used, the time of day, and the day of the week.
Contreras, Seth Regional Scale Dispersion Modeling and Analysis of Directly Emitted Fine Particulate Matter From Mobile Source Pollutants Using AERMOD PhD, Civil Engineering, 2015. 130 pp. Adviser: Michael G. McNally.
A large and growing body of literature associates proximity to major roadways with increased risk of many negative health outcomes and suggests that exposure to fine particulate matter may be a substantial factor. Directly emitted and non-reactive mobile source air pollutants such as directly emitted fine particulate matter can form large spatial concentration gradients along major roadways, in addition to causing significantly large temporal and seasonal variation in air pollutant concentrations within urban areas. Current modeling and regulatory approaches for minimizing exposure have limited spatial resolution and do not fully exploit the available data.
The objective is to establish a methodology for quantifying fine particulate matter concentration gradients due to mobile source pollutants and to estimate the resulting population exposure at a regional scale. A novel air dispersion modeling framework is proposed using EPA’s AERMOD with data from a regional travel demand model that can produce a high resolution concentration surface for a considerably large metropolitan area; in our case, Los Angeles County, California. We find that PM2.5 concentrations are highest and most widespread during the morning and evening commutes, particularly during the winter months. This is likely caused by a combination of stable atmospheric conditions during the early morning and after sunset in the evening and higher traffic volumes during the morning and evening commutes. During the midday hours concentrations are at their lowest even though traffic volumes are still much higher than during the evening. This is likely the result of heating during the day time which leads to unstable atmospheric conditions that cause more vertical mixing and lateral dispersion, reducing ground level PM2.5 concentrations by transport and dilution. With respect to roadway centerlines, PM2.5 concentrations drop off quickly, reaching relatively low concentrations between 150m to 200m from the center line of high volume roads. However, during stable atmospheric conditions (e.g., nighttime & winter season) concentrations remain elevated at distances up to 1,000m from roadway centerlines.
We will demonstrate the feasibility of our methodology and how integrating the dispersion modeling framework into the travel demand modeling process routinely performed when developing and analyzing regional transportation improvement initiatives can lead to more environmentally and financially sustainable transportation plans. Regional strategies that minimize exposure, rather than inventories, could be established, environmental justice concerns are easily identified, and projects likely to cause local pollution “hotspots” can be proactively screened out, saving time and money for the transportation agency.
A macroscopic relation between the network-level average flow-rate and density, which is known as the macroscopic fundamental diagram (MFD), has been shown to exist in urban networks in stationary states. In the literature, however, most existing studies have considered the MFD as a phenomenon of urban networks, and few have tried to derive it analytically from signal settings, route choice behaviors, or demand patterns. Furthermore, it is still not clear about the definition or existence of stationary traffic states in urban networks and their stability properties. This dissertation research aims to fill this gap.
I start to study the stationary traffic states in a signalized double-ring network. A kinematic wave approach is used to formulate the traffic dynamics, and periodic traffic patterns are found using simulations and defined as stationary states. Furthermore, traffic dynamics are aggregated at the link level using the link queue model, and a Poincare ́ map approach is introduced to analytically define and solve possible stationary states. Further results show that a stationary state can be Lyapunov stable, asymptotically stable, and unstable. Moreover, MFD is explicitly derived such that the network flow-rate is a function of the network density, signal settings, and route choice behaviors. Also the time for the network to be gridlocked is analytically derived.
Even with the link queue model, traffic dynamics are still difficult to solve due the discrete control at signalized junctions. Therefore, efforts are also devoted to deriving invariant continuous approximate models for a signalized road link and analyzing their properties under different capacity constraints, traffic conditions, traffic flow fundamental diagrams, signal settings, and traffic flow models. Analytical and simulation results show that the derived invariant continuous approximate model can fully capture the capacity constraints at the signalized junction and is a good approximation to the discrete signal control under different traffic conditions and traffic flow fundamental diagrams. Further analysis shows that non-invariant continuous approximate models cannot be used in the link transmission model since they can yield no solution to the traffic statics problem under certain traffic conditions.
For a signalized grid network, simulations with the link queue model confirm that important insights obtained for double-ring networks indeed apply to more general networks
Hernandez, Sarah Integration of Weigh-in-Motion and Inductive Signature Data for Truck Body Classification PhD, Civil Engineering, 2014. 247 pp. Adviser: Stephen G. Ritchie.
Transportation agencies tasked with forecasting freight movements, creating and evaluating policy to mitigate transportation impacts on infrastructure and air quality, and furnishing the data necessary for performance driven investment depend on quality, detailed, and ubiquitous vehicle data. Unfortunately, commercial vehicle data is either missing or expensive to obtain from current resources. To overcome the drawbacks of existing commercial vehicle data collection tools and leverage the already heavy investments into existing sensor systems, we present a novel approach of integrating two existing data collection devices to gather high resolution truck data – Weigh-in-motion (WIM) systems and advanced inductive loop detectors (ILD). Each source provides a unique data set that when combined produces a synergistic data source that is particularly useful for truck body class modeling. Since body configuration is closely linked to commodity carried, drive and duty cycle, and other operating characteristics, it is inherently useful for each of the above mentioned applications.
In this work we describe the physical integration including hardware and data collection procedures undertaken to develop a series of truck body class models. Approximately 33,000 samples consisting of photo, WIM, and ILD signature data were collected and processed representing a significant achievement over previous ILD signature models which were limited to around 1,000 commercial vehicle records.
Three families of models were developed, each depicting an increasing level of input data and output class resolution. The first uses WIM data to estimate body class volumes of five semi-trailer body types and individual predictions of two tractor body classes for vehicles with five axle tractor trailer configurations. The trailer model produces volume errors of less than 10% while the tractor model resulted in a correct classification rate (CCR) of 92.7%. The second model uses ILD signatures to predict 47 vehicle body classes using a multiple classifier system (MCS) approach coupled with the Synthetic Minority Oversampling Technique (SMOTE) for preprocessing the training data samples. Tests show the model achieved CCR higher than 70% for 34 of the body classes. The third and most complex model combines WIM and ILD signatures using to produce 63 body class designations, 52 with CCR greater than 70%. To highlight the contributions of this work, several applications using body class data derived from the third model are presented including a time of day analysis, average payload estimation, and gross vehicle weight distribution estimation.
Yuan, Daji Incorporating Individual Activity Arrival and Duration Preferences within a Time-of-day Travel Disutility Formulation of the Household Activity Pattern Problem (HAPP) PhD, Civil Engineering, 2014. 112 pp. Adviser: Will Recker.
This dissertation provides modifications and extensions to the Household Activity Pattern Problem (HAPP) to help move existing formulations from a laboratory prototype toward a more useable activity-based demand modeling product. Previous research on HAPP has been based on a pickup and delivery problem with time window constraints (PDPTW), which does not lend itself easily to application that is compatible with an activity-based forecasting model. Meanwhile, other research on activity based modeling lacks of the integration of household decisions regarding time-of-day arrival, activity duration and traffic congestion effects on travel. We borrow concepts from economic research and consider that each household member tries to obtain maximum utility by choosing arrival time of activities, choosing activity duration while minimizing travel times and travel costs throughout the course of the day. Chapter 1 provides the introduction and motivation of this research. Chapter 2 reviews pertinent literature relative to the activity-based approach, the HAPP model, and positions the dissertation research relative to the existing state-of-the-art. In Chapter 3 we propose extensions to HAPP (UHAPP) that incorporate time of day activity arrival utility and the utility of activity duration into HAPP as decision variables. In Chapter 4 we introduce the travel time-dependent household activity pattern problem model (TUHAPP), which extends the ability of HAPP to capture the time-of-day (TOD) difference in travel times and costs. In Chapter 5 we develop a framework using TUHAPP (UHAPP) as a regional activity-based demand model with a household travel survey. Chapter 6 provides conclusions and future research.
Lakew, Paulos Ashebir Essays on Air Cargo Cost Structures, Airport Traffic, and Airport Delays: Panel Data Analysis of the U.S. Airline Industry PhD, Transportation Science, 2014. 161 pp. Adviser: Jan K. Brueckner
The present thesis is comprised of four essays that address important gaps in passenger- and cargo-airline research. Seminal studies in airline economics that rely on cross-section methods make critical homogeneity assumptions and preclude time-specific effects. The essays in this thesis use panel data, which allow for certain assumptions made by cross-sectional studies to be relaxed, while shedding light on the intertemporal features of air transport.
The first chapter investigates the cost structure of air cargo carriers by applying a total cost model used in passenger-airline studies. Using quarterly panel data (2003-2011) on the domestic operations and costs of FedEx Express and UPS Airlines, empirical results indicate that the air cargo industry exhibits increasing returns to traffic density and constant returns to scale. Accounting for carrier-specific differences in cost structure and network size, FedEx is found to be more cost efficient than UPS (a finding that is reversed when network size is not controlled). Individually, UPS exhibits substantial economies of density and constant returns to scale while FedEx's cost structure is characterized by weak economies of density and constant returns to scale. Both carriers exhibit economies of size.
The next three chapters embody papers that use quarterly panel data of city-level air traffic, airline delay, and socioeconomic variables. Spanning 10 years (2003-2012), the panel structure of the data permits the use of fixed effects to control for city-specific heterogeneity.
The second chapter presents a paper prepared for the Airport Cooperative Research Program (ACRP). The study demonstrates the within-city traffic impacts of urban size, employment composition, and wages, providing new insights into the determinants of passenger and air cargo traffic. The essay also confirms that airport traffic is proportional to population, and that service-sector employment and higher wages induce passenger travel and goods movement. A city's share of manufacturing employment, however, only impacts air cargo traffic. Passenger enplanements exhibit more sensitivity to the proportion of urban workers providing non-tradable services, compared to the share of workers in tradable service jobs.
The third chapter, co-authored with Andre Tok, examines the determinants of air cargo traffic in California. The study uses a shorter 7-year panel (2003-2009), and shows that service and manufacturing employment impact the volume of outbound air cargo. Total (domestic) air cargo traffic is found to grow faster than (proportionally to) population, while wages play a significant role in determining both total and domestic air cargo movement. Metro-level air cargo tonnage are also forecasted for the years 2010-2040, indicating that California's total (domestic) air cargo traffic will increase at an average rate of 5.9 percent (4.4 percent) per year in that period.
The final chapter is co-authored with Volodymyr Bilotkach, and it provides the first evidence on the impact of airline delays on urban-sectoral employment. Controlling for unobserved city-specific differences, the empirical estimates of the effects of air traffic on total employment are comparable to previously reported measures. However, service-sector employment is found to be less sensitive to air traffic than other studies suggested. New evidence confirming that delays have a negative impact on employment is also provided, a finding that is robust to various model specifications.
Abdel-Salam, Gaby Active Travel, Built Environment and Transit Access: A Micro-Analysis of Pedestrian Travel Behavior PhD, Transportation Science, 2014. 285 pp. Advisers: Douglas Houston and Michael G. McNally
The introduction of Senate Bill (SB 375) in 2008 stimulated more research linking travel behavior to the built environment. Smart growth tools mandated by this bill aim to reduce vehicle miles traveled (VMT), greenhouse gas (GhG) emissions and promote alternative modes to motorized travel. These tools encompass an array of land use improvements that are expected to influence active travel. Potential changes in the built environment may impact the frequency, amount and even the selection of routes for walking.
Data used in this dissertation was obtained from Phase I of the Expo Study, a three--‐phase travel survey of residents living near the Expo Light Rail Line in Los Angeles, CA. Respondents carried GPS devices and accelerometers to track locations and activity levels; and completed seven--‐day trip logs. Phase I of the survey was administered in Fall 2011, prior to the introduction of the Expo Line in April 2012.
This dissertation is comprised of three research topics. The first topic uses a “place--‐oriented” approach to examine where active travel occurs in neighborhoods adjacent to the Expo Light Rail Line. This chapter is based on the Behavioral Model of Environments, which emphasizes the influence of the physical environment on individuals’ travel behavior and route choices. Results indicate that the routes selected by pedestrians have higher densities of commercial and retail centers and better access to more transit stations.
The second research topic uses an ecological modeling approach. Multilevel analysis of the effects of the built environment on active transport was performed in three geographic levels of aggregation near respondents’ homes. Examination of land uses at the half--‐mile extent yield the least number of significant results. In contrast, land uses examined at the segment--‐level and quarter--‐mile distance from homes emphasize the importance of street connectivity and green space on increasing transport--‐related physical activity (TPA). This suggests the importance of analyzing the data at finer geographic levels.
The third research topic proposes a practical methodology of pedestrian route analysis in which observed GPS--‐tracked routes were examined and compared to GIS--‐ simulated shortest paths. The two route types were compared over deviations in trip--‐ level travel indices, respondents’ socio--‐demographic traits, time of day variations and differences in objectively measured built environment features along both sets of routes. Results suggest that observed routes diverged more from shortest paths with increasing distance and were more circuitous beyond the 2.4--‐mile threshold. Most walks were completed after the AM Off Peak time. With the exception of the Evening time, observed routes were found to be much longer in all time periods especially in the AM Peak time. Moreover, higher densities of commercial centers, local businesses and green spaces were observed more for GPS--‐tracked routes than for shortest paths. These routes also had more street intersections and transit stops. Overall, results imply that pedestrians selected routes that were longer than the respective shortest paths and that may have been due to greater access to amenities and activity centers.
Allahviranloo, Mahdieh Inferring and Replicating Activity Selection and Scheduling Behavior of Individuals PhD, Civil Engineering, 2014. 166 pp. Adviser: Will Recker
Understanding the choices that each individual in the population makes regarding daily plans and activity participation behavior is crucial to forecasting spatial-temporal travel demand in the region. The focus of this dissertation is developing a comprehensive mathematical/statistical framework to infer and replicate travel behavior of individuals in terms of their socio-demographic profiles. The framework comprises series of distinct modules that employ statistical segmentation, Bayesian econometrics, data mining, and optimization techniques to predict individuals’ activity types, activity frequencies, and the travel linkages that make them possible.
The key advantages of the model are: first; providing the likely content of activity agenda as part of the inference procedure, second; integrating transportation network topology within activity scheduling step, and third; integrating modal components. As part of the dissertation, a Graphical User Interface was developed for practical application of the model in transportation agencies. The data used for the analysis is the California Household Travel Survey data, 2000-2001. Testing the entire modeling system on an out-of-sample population—15% of the entire sample— shows that the model is able to predict on average 80.3% of daily activities of individuals correctly.
Bhagat, Ankoor Simulation Study of Day‐Night Variations in Emissions Impacts and Network Augmentation Schemes: An Application to PierPASS Policy for Port Trucks in California PhD, Civil Engineering, 2014. 193 pp. Advisers: Jean-Daniel Saphores and R. Jayakrishnan
Freight operations are critical to our prosperity, but they also generate substantial external costs in the form of additional congestion, air pollution, and health impacts. Unfortunately these external costs are not well understood. In this dissertation, I focus on the drayage trucks that serve the San Pedro Bay Ports (or SPBP, i.e. the Ports of Los Angeles and Long Beach in Southern California), which is the largest port complex in the country. Freight routes providing access to the SPBP comprise a major rail-line (the Alameda Corridor) flanked by the I-110 and I-710 freeways, which both carry thousands of trucks per day. A number of policies have been implemented to reduce emissions on the ocean-side (e.g., limiting ship speeds and managing their queues) and in the Ports (e.g., providing power to docked ships so they do not have to run their engines). On the land-side, two policies were implemented: the Clean Trucks Program, which regulates drayage truck emissions and provides funds for their upgrade, and the PierPASS program (the focus of my dissertation), which shifts drayage trucks traffic from mid-day and peak hours to the evening and night hours. However, external costs from drayage trucks remain a major concern for communities adjacent to the ports because they bear a disproportionate fraction of the health impacts (respiratory and cardiovascular illness, cancer, and premature death) associated with the pollution generated by ports operations. In this context, my dissertation analyzes some of the benefits of shifting freight traffic to off-peak periods with an emphasis on congestion, air pollution (NOx, and PM) and related health impacts, using an innovative approach that expands microscopic traffic simulation model. My results will inform policy makers concerned with crafting cleaner logistics policies.
A new framework for analyzing choice set formation for route choice models is presented and an algorithm is proposed. The algorithm is tested against a sample of GPS data for heavy trucks for the State of California. The results are presented in detail along with an analysis of both their qualitative and quantitative merits.
A new algorithm for the route choice problem is also presented and its results analyzed against the state of the practice and state of the art. This new algorithm, ReMULAA, is also the first known closed solution algorithm for the route choice problem using the Multinomial Logit Model (MNL) for an entire class of networks (Directed Acyclic Networks) without explicit route enumeration. A correction for the MNL model to account for route overlapping is also presented and the results compared with other state-of-the-art route choice algorithms. The results of the application of ReMULAA in a real world model are also presented and its advantages discussed.
This dissertation assesses costs and benefits of two recent public rail transit systems in Kaohsiung, Taiwan’s second largest city: Kaohsiung’s mass rapid transit (MRT) system, which was completed and inaugurated in 2008 and Kaohsiung light rail transit (LRT) loop line, which is now under construction. I first focus on the benefits of the opening of Kaohsiung’s MRT system as reflected in the price of apartments with elevators. I combine two stage least squares with geographically weighted regression to analyze transactions of apartments with elevators in 2007 and 2009. This approach allows accounting for the joint determination of time-on-market information (TOM) and price while allowing hedonic parameters to vary spatially. Results show that the opening of the MRT had a statistically significant and positive impact on the value of apartments with elevators. However, accounting for TOM has a negligible impact on my results.
Second, I apply the theory of real options to capture uncertainty in operating revenues and costs in the context of build-operate-transfer (BOT) and operate-transfer (OT) contracts for Kaohsiung’s LRT loop line project. Unlike the traditional net present value (NPV) approach, real options analysis includes option values embedded in a project. Here, I rely on the binomial pricing approach to explore the value of the options to abandon and to expand the project. My findings show that the options to abandon or expand the LRT system are not sufficient to make a BOT contract attractive to a private firm, even under the best case scenario; however, accounting for the value of these options makes an OT contract at least 10% more attractive. These results show that accounting for uncertainty in large urban transportation projects can be important although the value of flexibility may not be sufficient to offset large construction costs.
A method for accurate emissions estimation that will contribute to promoting public health has been increasingly important. The purpose of this study is to develop a novel method that is designed to make accurate real-time emissions estimation from individual vehicles on freeways possible. The benefit of this method is that it can overcome the weakness of macroscopic emissions estimation methods, which underestimated emissions.
The most distinguishing feature of the Speed Profile Estimation (SPE) method is that it uses a speed profile (SP) that is generated by the sum of a basic SP (BSP), which is calculated by the basic travel information of an individual vehicle obtained from vehicle reidentification (REID), and a residual SP (RSP), which is estimated by categorized traffic information.
In order to estimate RSP this research employs Autoregressive (AR) model and Fourier series (FS). And to find the parameters of RSP, the total absolute difference between actual SP emissions and estimated SP emissions was optimized by genetic algorithm. For this, parameters are calculated for all possible combinations of three categorizations and clusters by K-mean clustering. Individual vehicle trajectories from two freeways, US101 and I-80, were provided by the Next Generation Simulation (NGSIM) dataset. US101 was examined for calibration, and I-80 for validation. And then, transferability tests were conducted for various section distances to verify model transferability. Finally, REID is simulated with low vehicle signatures match rates to test its applicability to real situations.
Unlike previous methods, the SPE is notable for its real-time, transferable, reliable, and cost efficient emissions estimation. The calibration and validation account only 4.0 % and 4.1 % MAPEs, respectively. Moreover, transferability tests showed that MAPEs are lower than 4.4 % in both longer and shorter section distances. Furthermore, REID simulation increases only 0.2 % MAPE even in low vehicle signatures match rates, which is lower than 5 % MAPE in emissions estimation.
Any signal-like formulation other than AR or FS can perform better emissions estimation when it replaces the RSP. Also, in this research the SPE method was calibrated only for LOS F, when it is arguably of greatest value, but further research should be coordinated to extend the models in other possible traffic conditions such as LOS A~E.
The purpose of my dissertation is to explore how gender interacts with other factors such as personal attitudes, earning power, household structure, and the built environment to influence travel behavior, with a focus on whether these factors strengthen or relieve the constraints women face when making travel choices. In this context, my dissertation is organized around three separate case studies in California that rely on various discrete choice econometric models.
Results from my first case study indicate that chauffeuring trips in two-adult households with children are intensely gendered, and women bear most of the chauffeuring burden. It is partly because women’s income earning potential is generally lower than that of their male partners. However, living in neighborhoods with access to bus stop and with less single-family housing can reduce this gender chauffeuring gap. It suggests that compact urban development and better bus service may yield social benefits that help alleviate women’s household burdens.
In my second case study, I find that mothers are more likely to extend their greater concerns about traffic safety to their children, which in turn reduces the chance that their children will walk or bike to school. However, mothers bear most of the burden to chauffeur their children to school not because they worry more, but because chauffeuring children is still seen more as a mother’s responsibility. It suggests that interventions targeting an increase in children’s active commuting to school should focus on the concerns of mothers, especially as they relate to traffic characteristics.
My findings in the third case study reveal that both environmental and safety concerns are associated with sustainable travel behavior, but the influence of safety concerns is more prominent and women have greater safety concerns. Moreover, proximity to transit service can increase sustainable travel behavior, but having higher safety concerns can totally offset this effect. For women with higher safety concerns, the reduction is even greater. It suggests that to encourage sustainable travel behavior, reducing personal safety concerns about transit use may be more effective than increasing public environmental awareness, especially for attracting potential female riders.
Ranaiefar, Fatemeh Interregional Commodity Flow Model Using Structural Equation Modeling: Application to the California Statewide Freight Forecasting Model PhD, Transportation Science, 2013. 120 pp. Adviser: Michael G. McNally.
Freight forecasting models are data intensive and may require many explanatory variables to achieve prediction accuracy. One problem, particularly in the United States, is that public data sources are usually available only at highly aggregate geographic levels, while models with more disaggregate geographic levels are required for regional freight transportation planning. A second problem is that supply chain effects are often ignored or modeled with economic input-output models which lack explanatory power. This study addresses these challenges by considering a Structural Equation Modeling approach, that is not confined to a specific spatial structure as spatial regression models would be, and allows for correlations between industries. The goal of the proposed methodology is to design a reliable and policy sensitive modeling framework for long term commodity flow forecasting that makes the best use of public available data sources. Practicality and improvement over previously available freight generation and distribution models are the highlights of this approach.
There are two primary developed in this study. The first one is a structural commodity generation model. The second model is the Structural Equations for Multi-Commodity OD Distribution (SEMCOD) model. The models are specified and estimated based on FAF3 data. It is shown that the proposed modeling framework provides a better fit to the data than independent regression models for each commodity. The three components of the models are: direct and indirect effects, supply chain elasticities at zone level and at origin-destination level, and intra-zonal supply-demand interactions. A validation of the geographic scalability of the model is conducted using a zoning system consisting of 97 county or sub-county zones in California.
Location-based services have been identified as a promising communication paradigm in highly mobile and dynamic vehicular networks. However, existing mobile ad hoc networking cannot be directly applied to vehicular networking due to differences in traffic conditions, mobility models and network topologies. On the other hand, hybrid architectures in vehicular networks, with ad hoc-based inter-vehicle and infrastructure-based vehicle-to-roadside communications, can facilitate robust and efficient communication services using geographical information. In this dissertation, we focus on the design and evaluation of location-based protocols and algorithms to improve scalability, efficiency, and resiliency in hybrid vehicular networks.
We first provide a cross-layer self-localization algorithm for moving vehicles. A new ultra-wide band (UWB) coding method, based on an orthogonal variable spreading factor and time hopping, is proposed for minimum interference during ranging. Then, a UWB based non-metric multidimensional scaling derives accurate and robust self-localization results. In addition, we employ an online compressive sensing scheme to count and localize sparse roadside units (RSUs) for war-driving applications. Online war-driving records received signal strength (RSS) values at runtime, and can recover the number and location of RSUs immediately based on far fewer noisy RSS readings.
After obtaining the location information of vehicles and RSUs, we address multiple channel scheduling in hybrid vehicular networks. We use the natural beauty of Latin squares to achieve fair and deterministic scheduling in micro-time scale for channel access and macro-time scale for channel assignment. A grid based scalable scheme is proposed to map Latin squares to grids for dynamic single-radio multi-channel scheduling. Another interference graph based scheme uses nodal location and social centrality to reflect the social behavior patterns related to access in vehicular networks, and then form adaptive clusters for multi-radio multi-channel scheduling.
We also investigate several vehicular environments, and propose corresponding location- and environment-aware data dissemination solutions. We first present an efficient on-demand bounce routing method in vehicular tunnels. It applies a hybrid signal propagation model and location-based forwarding metric to choose the best data dissemination strategy. Then, we design a hybrid routing scheme for robust and reliable data dissemination in urban transportation environments, in which the choice of communication method is dependent upon geographical connectivity, by taking network coding based multicast routing in dense network and opportunistic routing using carry and forward method in sparse network. In addition, we propose an online learning based knowledge dissemination in unmanned aerial vehicle (UAV) swarms under delay/disruption-tolerant networking, where each UAV adaptively chooses broadcast probability by learning link status. A fractionated Cyber-Physical System framework, based on partial ordering for knowledge sharing and colored Petri net for work flow, is implemented to achieve distributed knowledge management in UAV swarms.
Our extensive simulation and real testbed results show the robustness and efficiency of location-based services in vehicular networks with hybrid architectures.
Transportation has been a significant contributor to greenhouse gas and criteria air pollutant emissions. Emission mitigation strategies are essential in reducing transportation’s impacts on our environment. In order to effectively develop and evaluate on-road emissions reduction strategies, accurate quantification of emissions is the critical first step. The accuracy and resolution of the traffic measures needed by the emission models will directly affect the emission estimation results. This dissertation investigates the application of traffic detection technologies to deriving the traffic measures needed for accurate on-road emissions estimation.
The inductive vehicle signature (IVS) system is identified as the most promising technology to couple with EPA’s latest MOVES emission model for estimating emissions accurately. Models and algorithms based on the IVS detection system are developed to generate the two most important traffic measures for emission estimation: vehicle mix and average speed. The performances of the models are verified using real-world field data.
Although average speed has been the most common input into emission models, the MOVES model is capable of using second-by-second vehicle speed trajectories to estimate emissions more accurately. Crowd sourced GPS data can also be used by emission models like MOVES to estimate emissions. In this study, we aim to answer two most fundamental questions: 1) how to use the GPS data, and 2) how the penetration rate of the GPS probes affects the emission results. It is found that emissions can be estimated with high accuracy and reliability with even a very small penetration rate of GPS probes.
We conclude that the IVS detection system and GPS probe data can be successfully applied to estimate accurate and reliable on-road emissions estimation. Discussions on the application of the models developed in this study to various scenarios are included.
Kang, Jee Eun Integration of Locational Decisions with the Household Activity Pattern Problem and Its Applications in Transportation Sustainability PhD, Civil Engineering, 2013. 239 pp. Adviser: Will Recker.
This dissertation focuses on the integration of the Household Activity Pattern Problem (HAPP) with various locational decisions considering both supply and demand sides. We present several methods to merge these two distinct areas—transportation infrastructure and travel demand procedures—into an integrated framework that has been previously exogenously linked by feedback or equilibrium processes.
From the demand side, the Location Selection Problem for the Household Activity Pattern Problem (LSP-HAPP) is developed. LSP-HAPP extends the HAPP by adding the capability to make destination choices simultaneously with other travel decisions of household activity allocation, activity sequence, and departure time. From the supply side, the network decisions are determined as an integral function of travel demand rather than a given fixed OD matrix. The Location – Household Activity Pattern Problem (Location-HAPP), a facility location problem with full-day scheduling and routing considerations is developed. This is in the category of Location-Routing Problems (LRPs), where the decisions of facility location models are influenced by possible vehicle routings. Location-HAPP takes the set covering model as a location strategy, and HAPP as the scheduling and routing tool. The Network Design Problem is integrated with the Household Activity Pattern Problem (NDP-HAPP) as a bilevel optimization problem. The bilevel structure includes an upper level network design while the lower level includes a set of disaggregate household itinerary optimization problems, posed as HAPP or LSP-HAPP.
Utilizing the aforementioned models, two transportation sustainability studies are then conducted for the adoption of Alternative Fuel Vehicles (AFVs). From the demand, we measure the household inconvenience level of operating AFVs. From the supply side of the AFV infrastructure, Location-HAPP is applied to the incubation of the minimum refueling infrastructure required to support early adoption of Hydrogen Fuel Cell Vehicles (HFCVs).
The growth of urban vehicle traffic generates serious transportation and environmental problems in most countries of the world. Intelligent transportation systems (ITS) are effective means to solve basic traffic problems, such as driving safety, road congestion, disaster supplies, emissions, etc. Inter-vehicle communication (IVC) system is one of the most important components of ITS. In recent years, the rapid development of information technologies leads a revolution in IVC, enabling IVC be a powerful multifunctional system. However, there exist numerous challenges for IVC studies. This dissertation is aimed to address three urgent and critical issues in IVC: efficiency of information exchanging among connected vehicles, simulation methods, and IVC applications.
Information transmission efficiency, which can be measured by communication throughput or capacity, is a fundamental property of vehicular ad hoc networks. This dissertation theoretically analyzes communication throughputs, including broadcast and unicast communications, under discrete and continuous vehicular ad hoc networks (VANETs). We also examine influence of transmission range, influence ratio, market penetration rate of IVC-equipped vehicles, percentage of senders and traffic waves on throughputs. Furthermore, we derive a theoretical formulation to calculate communication capacities under uniform traffic streams. And, an integer programming (IP) model is improved to explore capacities in general traffic, and a genetic algorithm is constructed to search the solutions efficiently.
The second contribution of this dissertation is the development of a hybrid traffic simulation model to evaluate transportation systems incorporated with IVC technologies. As IVC-equipped vehicles are able to obtain more road information and they are controlled to pursue some objectives, they will behave differently from others, and transportation systems will become heterogeneous. This dissertation presents a hybrid traffic simulation model coupling microscopic and macroscopic models to address heterogeneity in transportation systems. In the model, equipped vehicles are regulated by a car-following model, while the other vehicles are described as continuous media with the Lighthill-Whitham-Richard (LWR) model. We analytically study the model on a single-lane road using a modified Godunov method. The hybrid model shows its potential of accurate wave propagation from individual vehicles to continuous traffic streams, and reversely; i.e., the model is capable of analyzing heterogeneous traffic. Moreover, consistency, stability and convergence of the hybrid model are carefully investigated. The model also shows the advancement of computational efficiency and control flexibility on traffic simulations.
Finally, for IVC applications in environment, we propose a green driving strategy to smooth traffic flow and lower pollutant emissions and fuel consumption. In this dissertation, we study constant and dynamic green driving strategies based on inter-vehicle communications. Generally, speed limit control in successful strategies guarantee a vehicle's speed profile be smooth while still following its leader during a relative long time period. A theoretical analysis of constant strategies demonstrates that optimal smoothing effects can be achieved when a speed limit is set to be close to but not smaller than average speed of traffic. We consider a dynamic strategy in which controlled vehicles share location and speed information based on a feedback control system. The influence of market penetration rate of equipped vehicles and communication delay on the strategy is also analyzed. Besides the development of the green driving strategy, we construct a green driving APP for smartphones on the Google Android platform and design a field experiment to check the feasibility of the strategy. The results are promising and support the advancements of IVC on reducing emissions and fuel consumption.
Freight transportation demand is a highly variable process over time and space. Two challenges in current regional freight forecasting are the lack of consideration of the space-time trade-offs and the lack of behaviorally-based models for temporally assigning annual commodity flows to daily flows. State-of-the-practice models typically use fixed factors for temporal assignment and do not address the tradeoffs between transport costs and inventory costs, which can aid in quantifying the impact of different land uses on monthly truck distributions or the impact of rising fuel costs on shipment frequency and warehousing needs. This dissertation work makes the first step toward explicitly modeling the freight temporal distributions and proposes a novel approach that adopts the concept of Network Economics and Economic Order Quantity (EOQ) inventory in an agent-based freight demand modeling framework.
Unlike other agent-based models that seek to replace the whole freight forecasting process, the proposed model relies on other aggregate models to generate annual distribution channels (commodity OD matrix) and monthly demand distributions by commodity type. This frees the model to focus on trade-offs between transport and inventory without having to bear the burden of limited disaggregate data for other choices.
The modeling framework is composed of two main components: (1) a supplier selection module to indicate the supply chain interactions and determine the order quantity from one firm to another firm while meeting the zone level flow constraints; (2) an EOQ-based inventory operation module to indicate the goods movement daily pattern and determine the daily firm-firm flows by modeling firms' inventory replenishment decisions. By aggregating the daily firm-firm flows back up to the zone level, we get the average zone-zone daily flows by commodity types as the final output.
The whole framework has been fully examined using the California data. A union of 6 datasets is utilized as inputs to model the daily flows of 503 firm groups in California during the 261 weekdays in year 2007. As one parameter of the normative model, the unit inventory holding cost has been calibrated with the given inventory data. A simple comparison of the model outputs with the fixed factor approach is conducted. Four use cases are presented to demonstrate the effectiveness of such a new model for freight transport analysis.
This dissertation describes a series of real-time vehicle routing problems with the associate optimization and simulation modeling for flexible passenger transport systems such as the High Coverage Point-to-Point Transit (HCPPT) and shared-taxi, which involve a sufficient number of deployed small vehicles with advanced information supply schemes to match real-time passenger demands and vehicle position for passenger transportation over large areas.
HCPPT is an alternate design for mass passenger transport developed in recent years at the University of California at Irvine. The designs rely on transfer hubs, trunk route connections between the hubs where the vehicles are non-reroutable, and local areas around the hubs where the vehicles are reroutable. First, we relax the restriction in the existing heuristic rules of HCPPT, expecting to yield higher efficiency for general cases. Optimization schemes are proposed for both trunk and local vehicle routing problems to consider global optimality for large-scale problems. Significantly, the new algorithms allow globally optimal vehicle movements over multiple-hubs, unlike the earlier designs that allowed travel only to the adjacent hubs. This in turn ensures that the scheme has scalability in large areas and has design flexibility in adjusting the distances between hubs. Second, for an efficient and productive taxi system of the conventional kind, a design of shared-taxi operation is proposed, which also can be potentially used for local area operations in HCPPT. Three algorithms are developed and compared with different objective functions.
Another contribution of this research is the development of a simulation platform targeting large-scale flexible point-to-point transit systems with various vehicle operation schemes. Traditionally, real-time DRT operations are simulated with commercial traffic simulators such as mesoscopic or microscopic simulation models, which is cumbersome because the available software were not designed for such real-time routed vehicle simulation, and also because they include details of less relevance to large-scale real-time Demand Responsive Transit (DRT) systems. The simulation studies in this research evaluate the vehicle routing algorithms through the proposed platform for Orange County, U.S.A. and Seoul, Korea.
Finally, this thesis studies two large-scale fleet applications of Electric Vehicles (EV) as a future transportation alternative, as the hub locations which are part of the designs developed in this research are particularly suitable as energy replenishment nodes. Since EVs have a limited driving range and need to visit charging stations frequently, this part mainly focuses on the vehicle charge replenishing schedules in conjunction with passenger pickup and delivery schedules and measures the benefits from combining EVs and DRT fleets.
In recent years the Clean Trucks Program (CTP) has been enacted at California’s San Pedro Bay Ports (SPBPs) of Long Beach and Los Angeles to help address major environmental issues associated with port operations. “Clean trucks” that utilized public funds to replace older polluting drayage trucks were required to be fitted with GPS units for compliance monitoring. Such GPS data collected by the clean drayage trucks provide a significant opportunity to investigate drayage truck tour behaviors distinct from general commercial vehicles.
With the background, this dissertation consists of three topics: 1) Tour Behavior of Clean Drayage Trucks; 2) Tour-Based Entropy Maximization Model of Drayage Trucks; and 3) Drayage Truck Tour Modeling Using the Inverse Selective Vehicle Routing Problem (InvSSVRP) in Southern California. As expected, the first step is to analyze GPS data for interpreting the drayage trucks’ characteristics. In the second and third steps, tour-based models are developed using aggregate and disaggregate level approaches.
An analytical framework is introduce for processing GPS data to both interpret the trip chaining of the clean drayage trucks, and to prepare sufficient tour data for clean truck modeling at the SPBPs. After analyzing data using the toolkit, one of the significant findings from the clean drayage truck behaviors is that the tours could be classified under four types, three of which contain repetitive trip patterns in a tour while the remainder tends to travel in circulative patterns to avoid visiting the same location multiple times. This provides both the answer that the current tour-based model cannot address drayage truck behavior and why tour-based modeling of the drayage trucks is developed separately.
Two other theoretical advances in the research are the development of tour-based models using an Entropy Maximization Algorithm and a Selective Vehicle Routing Problem.
For the aggregate level, the revised tour-based entropy maximization model upgrades the tour-based entropy maximization model by Wang and Holguín-Veras (2009) which mostly focuses on other commercial vehicles. After introducing new constraints regarding sequential visits to nodes, the clean drayage truck tour behavior can be well addressed.
At the disaggregate level, the SSVRP provides a utility-maximizing decision-making optimization framework under spatial-temporal constraints to explain observed truck patterns as activity participation analogous to household activity patterns. This would be impossible without the capability of the InvSSVRP to calibrate the objective coefficients and arrival time constraints such that observed patterns are optimal values. The nodes are sequence-expanded to allowing multiple visits at each node and divided into two arrival states (from depot or not from depot) in the SSVRP provide much more realism in capturing the drayage truck behavior.
To make better use of the two proposed models, the framework of each tour-based model estimation and forecasting process is illustrated. Lastly, several future topics of relevance to improving the tour-based models are discussed.
Lee, Gunwoo. Integrated Modeling of Air Quality and Health Impacts of a Freight Transportation Corridor PhD, Civil Engineering, 2011. 213 pp. Adviser: Stephen G. Ritchie.
Due to environmental concerns, transportation studies have extensively evaluated emission impacts associated with traffic operational strategies and transportation policies. However, the impact studies mainly relied on emission impacts found using demand forecasting models. Such planning models cannot capture individual vehicles’ interactions (i.e., lane changes or stop-and-go movements) or detailed traffic operations such as with traffic signals. These limitations often lead to under-estimated emissions while evaluating several policies. Even though many studies utilized microscopic traffic models to better estimate emissions, the studies have not considered further steps such as air quality estimation and health impact studies.
This research develops an integrated framework for evaluating air quality and health impacts of transportation corridors using a microscopic traffic model, a micro-scale emissions model, a non-steady state dispersion model, and a health impact model. The main advantage of this approach is to better estimate air quality and health impacts from vehicle interactions and detailed traffic management strategies.
As a case study, we evaluate air quality and health impacts of several scenarios associated with major transportation corridors accessing the San Pedro Bay Ports (SPBP) complex, California. The study context consists of two 20 miles-long major freight freeway corridors and nearby arterials, as well as line-haul rail along the Alameda corridor and several rail yards associated with the SPBP complex. For the scenarios, we consider a clean truck program, cleaner locomotives, and modal shifts compared to the 2005 baseline. All scenarios performed with the integrated framework have provided larger improvements of air quality and health impacts associated with transportation corridors than conventional frameworks using transportation planning models. However, the difference in air quality and health impacts from modal shift scenarios between clean trucks and locomotives are minor.
As exploratory research, pollution response surface models are developed. The main objective of the pollution response surface model is to avoid the high computational cost of the microscopic traffic model, which makes it difficult to estimate traffic for multiple days needed for evaluating emissions and health impacts over longer periods such a climate season. A conceptual framework for estimating pollution response surface models is proposed. Using a hypothetical network, response surfaces of NOX and PM are estimated.
Kopitch, Lima. An Analysis of the Impact of an Incident Management System on Secondary Incidents on Freeways – An Application to the I-5 in California PhD, Transportation Science, 2011 117 pp. Adviser: Jean-Daniel Saphores.
Accidents are the largest source of external costs related to transportation in the United States with annual costs estimated to exceed $200 billion per year. Incidents also create traffic backups and delays that can result in secondary incidents (i.e., collisions that occur as a result of other incidents). Although incident management has received a lot of attention from academics and practitioners alike, secondary incidents have so far been somewhat neglected.
The main purpose of this dissertation is to investigate empirically whether the implementation of changeable message signs (CMS), which are one Intelligent Transportation System tool, can reduce secondary collisions. After reviewing previously published methods for estimating secondary accidents, I implement a Binary Speed Contour Map approach to detect secondary incidents using PeMS data. I also estimate the extra time lost to congestion because of incidents.
My study area is a portion of Interstate 5 that stretches 74 miles from the Mexico-US border to Orange County, CA. This freeway has an average annualized daily traffic volume of 230,000 vehicles and fifty-five miles of it are equipped with CMS. My unique dataset includes incident data for 2008 combined with detailed weather data, elements of freeway geometry, and information about CMS usage.
I identify a total of 10,172 incidents in my study area in 2008. Using the BSCM approach, I find that 4.6 percent of collisions were secondary incidents. Moreover, my statistical model shows that incidents occurring during evening peak hours on Fridays are more likely to result in secondary crashes as do more severe incidents, areas with a complex geometry, wet pavement, and changeable message signs (CMS). The maximum effectiveness of a CMS is approximately 10.5 miles for a range of 21 miles. Finally, annual incident-related congestion is approximately 1.9 hours per freeway vehicle, which represents five percent of the 37 hours of annual traffic delay experienced by the average San Diego motorist.
Ayala, Roberto. Of Planes, Trains and Automobiles: Market Structure and Incentives for a more Efficient, Cleaner and Fairer Transportation System. PhD, Transportation Science, 2011 96 pp. Adviser: Jean-Daniel Saphores
The unifying theme of this dissertation’s three applications of economics to transportation is an attempt to make transportation more efficient, environmentally friendlier and fairer.
In my first essay, I apply game theory and the notion of Cournot equilibrium to transportation. I compare two networks, hub-and-spoke and a point-to-point network, which is served by two non-cooperative transportation firms. I find that the way in which two firms set their respective network, either direct indirect service, has an effect on their costs and profits.
In my second essay, I analyze the ownership of hybrid electric vehicles by U.S. households using the 2009 National Household Travel Survey to understand the impact of various government policies aimed at increasing hybrid vehicle ownership, such as granting access to high-occupancy vehicle lanes, tax credits, and parking incentives. I use a logit model; explanatory variables include socio-economic characteristics, along with urban form, as well as policy variables. Understanding which policies are most cost-effective at fostering HEV ownership would allow policy makers to make effective use of public resources.
In my third essay, I address equity in transportation by stratifying the NHTS into three income groups: low-income, middle-income and upper-income. The purpose is to determine whether income affects travel behavior. I analyze questions in the 2009 NHTS that were not available in previous NHTS surveys. These questions inquire about internet use, medical condition and physical activity. I also estimate a multinomial logit model and find that those living in poverty and who report having a medical condition are more likely to make medical trips. Upper-income individuals are more likely to report social and recreational trips, meal and trips labeled as “other.” Analyzing trips by income is important from an equity standpoint when allocating scarce public funds for transportation projects, since it tells us what income groups are likely to be affected by specific transportation projects.
Yang, Inchul. The Interplay of Urban Traffic Route Guidance, Network Control and Driver Response: A Convergent Algorithmic and Model-based Framework. PhD, Civil Engineering, 2011. 194 pp. Adviser: R. Jayakrishnan
There is recent increase in the use of private providers’ digital map and traffic information systems that have evolved mostly without much public sector influence. Some paradigm shift is needed for thinking about the directions of future developments that will show societal benefits also open up private-sector opportunities. In this context, we develop a multi-agent advanced traffic management and information systems (ATMIS) framework with day-to-day dynamics where private agencies are included as traffic information service providers (ISPs) together with public agencies handling the traffic control and the users (drivers) as the decision-makers.
The emergence of private ISPs makes it possible to obtain path-based data via retrieval of individual trajectory diaries and current position information from their subscribers. This can bring about the development of new path-based ATMIS algorithms that are capable of taking into account the routing effects of advanced traveler information systems (ATIS). Under the assumption that the traffic management center (TMC) has some (even approximate) knowledge of the ISPs’ optimal strategies, it is possible to design optimal route guidance and control strategies (ORGCS) taking into account the anticipated ISP reactions in terms of route-level flows. In light of these issues, we develop a routing-based real-time cycle-free network-wide signal control scheme (R2CFNet) that uses path-based data. Another theoretical advance in the research is in the development of a modeling scheme that uses a new optimization algorithm for a convergent simulation-based dynamic traffic assignment (DTA) model. This model incorporates a Gradient Projection (GP) algorithm, as opposed to the traditionally-used Method of Successive Averages (MSA), and it displays significantly better convergence characteristics. A consistent day-to-day dynamic framework is also developed, incorporating an elaborate microscopic simulation model to capture traffic network performance, to study network dynamics.
The results of parametric simulations have shown that the proposed framework is capable of effectively capturing the effects of the interplay of urban traffic route guidance, network control and user response. An appropriate combination of ATIS market penetration rate and signal control settings could divert some portion of travel demand to different routes. This is achieved by constraining the signal settings to conform to certain longer-term strategies. The performance and efficiency of the components of the proposed framework such as the DTA model, the day-to-day dynamics model and the R2CFNet control scheme have been investigated through various numerical experiments that show promising results. Lastly, several future topics of relevance to the framework are discussed.
Traffic congestion and accidents continue to take a toll on our society with congestion causing billions of dollars in economic costs and millions of traffic accidents annually worldwide. For many years now, transportation planners have been pursuing an aggressive agenda to increase road safety through Intelligent Transportation System initiatives. Vehicular Ad hoc Network (VANET) based information systems have considerable promise for improving traffic safety, reducing congestion and increasing environmental efficiency of transportation systems. To achieve the future road safety vision, time-sensitive, safety-critical applications in vehicular communication networks are necessary. However, there are numerous technical hurdles for deploying VANET on the road network and its full potential will not be realized until the issues related to communication reliability, delay and security are solved.
VANET is a specific type of mobile ad hoc network (MANET) with unique characteristics that are different from a general MANET. These attributes include the traffic conditions (network density), mobility model (vehicle movements) and the network topology (road layout) imposed by the underlying transportation system. In this dissertation, we study broadcasting for VANETs that are applicable to many traffic safety applications. We investigate ways to improve reliability and reduce delay under numerous traffic conditions (free flow and congested flow traffic scenarios). Further, we incorporate vehicular traffic information to increase communication efficiency in dynamic vehicular networks. We believe that the contributions in this dissertation will be of interest to both the computer networking and transportation research communities.
With advances in computation and sensing, real-time adaptive control has become an increasingly attractive option for improving the operational efficiency at signalized intersections. The great advantage of adaptive signal controllers is that the cycle length, phase splits and even phase sequence can be changed to satisfy current traffic demand patterns to a maximum degree, not confined by preset limits. To some extent, traffic-actuated controllers are themselves “adaptive” in view of their ability to vary control outcomes in response to real-time vehicle registrations at loop detectors, but this adaptability is restricted by a set of predefined, fixed control parameters that are not adaptive to current conditions. To achieve the functionality of truly adaptive controllers, a set of online optimized phasing and timing parameters are needed.
This dissertation proposes a real-time, on-line control algorithm that aims to maintain the adaptive functionality of actuated controllers while improving the performance of signalized networks under traffic-actuated control. To facilitate deployment of the control, this algorithm is developed based on the timing protocol of the standard NEMA eight-phase full-actuated dual-ring controller. In formulating the optimal control problem, a flow prediction model is developed to estimate future vehicle arrivals at the target intersection, the traffic condition at the target intersection is described as “over-saturated” throughout the timing process, i.e., in the sense that a multi-server queuing system is continually occupied, and the optimization objective is specified as the minimization of total cumulative vehicle queue as an equivalent to minimizing total intersection control delay. According to the implicit timing features of actuated control, a modified rolling horizon scheme is devised to optimize four basic control parameters—phase sequence, minimum green, unit extension and maximum green—based on the future flow estimations, and these optimized parameters serve as available signal timing data for further optimizations. This dynamically recursive optimization procedure properly reflects the functionality of truly adaptive controllers. Microscopic simulation is used to test and evaluate the proposed control algorithm in a calibrated network consisting of thirty-eight actuated signals. Simulation results indicate that the proposed algorithm has the potential to improve the performance of the signalized network under the condition of different traffic demand levels.
Comprehensive Assessment of Managed Lane Performance and Characteristics Managed lanes that include high occupancy vehicle (HOV) and high occupancy and toll (HOT) lanes have been conducted for decades. Although being regarded as efficient and sustainable transport, managed lanes face such undiscovered issues as their performance regarding speed dispersion, equilibrium relationships between managed lanes and general purpose (GP) lanes in terms of speed and level of service, and joint evaluation of managed lane elements like eligibility, access control, and pricing. The goal of this dissertation is to provide theoretical and practical approaches to assessing managed lane operations under four modules, namely speed dispersion analysis, speed equilibrium analysis, lane management hot spot analysis, and optimal managed lane policy assessment. The first module correlates speed dispersion with the fundamental traffic flow parameters, and reveals that the coefficients of variation of speed for HOV and GP lanes are exponential with occupancy, negative exponential with space mean speed, and two-phase linear to flow, while the standard deviations of speed for both lanes do not display any simple regression form of either occupancy, space mean speed, or flow. The second module proposes two HOV schemes respectively under lane utilization and travel time savings for speed equilibrium between HOV and GP lanes. The schemes present distinct speed pairs by congestion level, but speed of HOV lanes is identically ensured no less than GP lanes under both schemes. The second module also covers an HOT scheme that adopts value of time and value of reliability to formulate HOT tolls with respect to speed of GP lanes. The third module identifies lane management and congestion hot spots by contrasting the level of service of managed lanes and GP lanes in a deterministic or stochastic way. The case study indicates that lane management hot spots are spatially and temporally dynamic, and a non-hot spot less likely turns to a congestion hot spot without being a lane management hot spot as transition, or vise versa. The last module develops two macroscopic approaches to screening the policy combination set of managed lanes, and eliminates the combinations by 60% in the selected scenario. Finally, the optimal/non-inferior policies for non-eliminated combinations are verified by solving such a case as a multi-objective binary integer linear programming problem.
Strategies, models, and algorithms facilitating such models are explored to provide transportation network managers and planners with more flexibility under uncertainty. Network design problems with non-stationary stochastic OD demand are formulated as real option investment problems and dynamic programming solution methodologies are used to obtain the value of flexibility to defer and re-design a network. The design premium is shown to reflect the opportunity cost of committing to a “preferred alternative” in transportation planning. Both network option and link option design problems are proposed with solution algorithms and tested on the classical Sioux Falls, SD network. Results indicate that allowing individual links to be deferred can have significant option value.
A resource relocation model using non-stationary stochastic variables as chance constraints is proposed. The model is applied to air tanker relocation for initial attack of wildfires in California, and results show that the flexibility to switch locations with non-stationary stochastic variables providing 3-day or 7-day forecasts is more cost-effective than relocations without forecasting.
Due to the computational costs of these more complex network models, a faster converging heuristic based on radial basis functions is evaluated for continuous network design problems for the Anaheim, CA network with a 31-dimensional decision variable. The algorithm is further modified and then proven to converge for multi-objective problems. Compared to other popular multi-objective solution algorithms in the literature such as the genetic algorithm, the proposed multi-objective radial basis function algorithm is shown to be most effective.>
The algorithm is applied to a flexible robust toll pricing problem, where toll pricing is proposed as a strategy to manage network robustness over multiple regimes of link capacity uncertainty. A link degradation simulation model is proposed that uses multivariate Bernoulli random variables to simulate correlated link failures. The solution to a multi-objective mean-variance toll pricing problem is obtained for the Sioux Falls network under low and high probability seasons, showing that the flexibility to adapt the Pareto set of toll solutions to changes in regime – e.g. hurricane seasons, security threat levels, etc – can increase value in terms of an epsilon indicator.
With soaring oil prices and growing concerns for global warming, there is increasing interest in the environmental performance of transportation systems. This dissertation contributes to this growing literature through three independent yet related projects essays that deal with transportation technology, infrastructure, and policy.
My first essay analyze the increasing interest for hybrid cars by Californians based on a statewide phone survey conducted in July of 2004 by Public Policy Institute of California (PPIC) using discrete choice models. Results suggest that the possibility for single drivers to use hybrid vehicles in HOV lanes is more important than short term concerns for air pollution, support for energy efficiency policies, long term concerns for global warming, education, and income. This suggests that programs designed to improve the environmental performance of individual vehicles need to rely on tangible benefits for drivers; to make a difference, they cannot rely on environmental beliefs alone.
The second essay is concerned with assessments of Travel Demand management (TDM) policies, which have been used to deal with congestion, air pollution, and now global warming. I compare two TDM programs: Rule 2202 (The on-road motor vehicle mitigation options in southern California) and the Commute Trip Reduction Program (CTR) in Washington State. My results reveal that after 2002, the impacts of Rule 2202 are mixed. Commuters’ modal choices are affected by worksite characteristics but only two (out of six) basic strategies effect the change in average vehicle ridership (AVR). Moreover, the level of subsidies appears to play an important role in commuting behavior. In Washington State, location has an impact on AVR and combinations of location and employee duties influence the single occupant vehicle index. Details of the CTR and its relative success suggest that there is room for improving Rule 2202 to make it friendlier to businesses and more effective.
Finally, I examine the health impacts of NO x (nitrogen oxides) and PM (particulate matter) generated by trains moving freight through the Alameda Corridor to and from the Ports of Los Angeles and Long Beach. After estimating baseline emissions for 2005, I examine two scenarios: in the first one, I assume that all long-haul and switching locomotives are upgraded to Tier 2 (from Tier 1); in the second scenario, all Tier 2 locomotives operating in the study area are replaced with cleaner, Tier 3 locomotives. I find that mortality from PM exposure accounts for the largest component of health impacts, with 2005 annual costs from excess mortality in excess of $40 million. A shift to Tier 2 locomotives would save approximately half of these costs while the benefits of shifting from Tier 2 to Tier 3 locomotives would be much smaller. To my knowledge, this is the first comprehensive assessment of the health impacts of freight train transportation in a busy freight corridor.
Joh, Kenneth. Unraveling the Complexity of Land Use and Travel Behavior Relationships: A Four-Part Quantitative Case Study of the South Bay Area of Los Angeles. PhD, Planning, Policy and Design, 2009. 236 pp. Adviser: Marlon G. Boarnet
Characteristics of the built environment, such as the mixture of land uses, transportation infrastructure, and neighborhood design, have often been associated with reduced automobile use and increased walking and transit use. However, a significant gap remains in our understanding of travel behavior, especially with respect with social environmental and attitudinal factors influencing travel, such as crime rates and the perceptions of walking. This dissertation, comprised of four empirical essays, explores the complex relationships between the built and social environment and neighborhood travel by focusing on non-work travel for individuals sampled from eight communities in the South Bay area of Los Angeles County.
In the first essay, I examine claims made by proponents of New Urbanism that traditional neighborhood designs promote walking and discourage driving by comparing automobile and walking trip rates for mixed-use centers and auto-oriented corridors. The results showed no discernable differences in individual driving trips between these two types of neighborhoods while more walking trips were reported in mixed-use centers. Therefore, the results both support and challenge New Urbanist claims.
The second essay examines the interactions between race/ethnicity, demographic change, and travel behavior by comparing driving and walking trips across racial and ethnic groups. The results showed that African-Americans took fewer driving trips and Asians walked less compared to non-Hispanic whites, and that Hispanics who walk are more sensitive to demographic changes in their neighborhood than other groups.
The third essay focuses on crime and perceptions of safety and how they impact walking behavior. After taking sociodemographic and built environment factors into account, violent crime rates had a strong deterrent effect on walking across race, income, and gender groups, while perceptions of neighborhood safety varied.
In the fourth essay, I focus on whether the built environment encourages walking above and beyond individuals’ attitudes toward walking. By comparing individuals with positive attitudes toward walking with those with neutral or negative attitudes, the results showed that individuals with positive attitudes were more responsive to built environment characteristics than those held negative attitudes. These findings suggest differences in walking behavior are more strongly shaped by personal attitudes than the built environment.
Use of advanced traffic control systems ranks as one of the most cost-effective actions for urban transportation improvements to mitigate total delay and alleviate fuel consumption and air pollution. Nonetheless, Adaptive Signal System, the most advanced type of traffic control designed for real-time traffic responsive operations, is not widely accepted in field implementation. Benefits of such systems are not fully realized yet, mainly because of the large cost for installment and maintenance of required sensor systems for traffic forecast. Moreover, even with the sensor systems, the performance still suffers due to inaccurate prediction caused by the limitation of data sources and deficiencies in the control algorithms.
Based on these observations, this study developed the applications of emerging data sources in traffic control system. Traffic parameters are collected under the new traffic information system such as a Persistent Traffic Cookies (PTC) system conceptually proposed at UC Irvine using wireless communication between a vehicle and a roadside hardware. With the preliminary study results under the system, this study develops traffic control schemes with the traffic forecast resulting from the PTC system. Initially, general methods are presented to generate required input, that is path-based traffic variables such as the turning flows and travel time from PTC data. The inputs were implemented in two different traffic control schemes; subnetwork definition for area-control and signal optimization scheme in network-level. The relevant spatial boundary for area-control is determined by a systematic approach on the basis of traffic dynamics estimated by the PTC data. Basically, the approach is to group multiple interconnected intersections with strong control dependencies on each other, which can be measured by the path flow among the intersections. Another application is a signal optimization scheme at the network-level under the assumption of fully decentralized control embedded with indirect signal coordination consideration. Local optimization was accomplished by a Dynamic Programming approach incorporating with a modified Rolling Horizon Scheme and network-wide coordination was indirectly achieved by iterative approach with repeated local optimizations.
For an evaluation of proposed control scheme, a simulation study was presented using Irvine Triangular Network constructed in microscopic simulation software. Results show that the proposed scheme is capable of reducing total delays in a network, in comparison to Actuated Signal Control already installed in the study network. It is also shown that the scheme that incorporates certain modified rolling horizon methods performs better than that with a more conventional rolling horizon method.
A spectrum of traffic engineering and modern transportation planning problems requires the knowledge of the underlying trip pattern, commonly represented by dynamic Origin-Destination (OD) trip tables. In view of the fact that direct survey of trip pattern is technically problematic and economically infeasible, there have been a great number of methods proposed in the literature for updating the existing OD tables from traffic counts and/or other data sources. Unfortunately, there remain several common theoretical and practical aspects which impact the estimation accuracy and limit the use of these methods from most real-world applications. This dissertation itemizes and examines these critical issues. Then, the dissertation presents the developments, evaluations, and applications of two new frameworks intended to be used with the current and near-future data, respectively.
The first framework offers a systematic and practical procedure for preparing dynamic demand inputs for microscopic traffic simulation under planning applications with an estimation module based solely on traffic counts. Under this framework, the traditional planning model is augmented with a filter traffic simulation step, which captures important spatial-temporal characteristics of route and traffic patterns within a large surrounding network, to improve the flow estimates entering and leaving the final microscopic simulation network. A new bounded dynamic OD estimation model and a solution algorithm for solving a large problem are also proposed.
The second framework utilizes additional information from small probe samples collected over multiple days. There are two steps under this framework. The first step includes a suite of empirical and hierarchical Bayesian models used in estimating time-dependent travel time distributions, destination fractions, and route fractions from probe data. These models provide multi-level posterior parameters and tend to moderate extreme estimates toward the overall mean with the magnitude depending on their precision, thus overcoming several problems due to non-uniform (over time and space) small sampling rates. The second step involves a construction of initial OD tables, an estimation of route-link fractions via a Monte Carlo simulation, and an updating procedure using a new dynamic OD estimation formulation which can also take into account the stochastic properties of the assignment matrix.
Uncertainties in transportation capacity and cost pose a significant challenge for both shippers and carriers in the trucking industry. In the practice of adopting lean and demand-responsive logistics systems, orders are required to be delivered rapidly, accurately and reliably, even under demand uncertainty. These tougher demands on the industry motivate the need to introduce new instruments to manage transportation service contracts. One way to hedge these uncertainties is to use concepts from the theory of Real Options to craft derivative contracts, which we call truckload options in this dissertation. In its simplest form, a truckload call (put) option gives its holder the right to buy (sell) truckload services on a specific route, at a predetermined price on a predetermined date. The holder decides if a truckload option should be exercised depending on information available when the option expires.
Truckload options are not yet available, however, so the purpose of this dissertation is to develop a truckload options pricing model and to show the usefulness of truckload options to both shippers and carriers. Since the price of a truckload option depends on the spot price of a truckload, we first model the dynamics of spot rates using a common stochastic process. Unlike financial markets where high frequency data are available, spot prices for trucking services are not public and we can only observe some monthly statistics. This complicates slightly the estimation of necessary parameters, which we obtain via two independent methods (variogram analysis and maximum likelihood), before developing a truckload options pricing formula. A numerical illustration based on real data shows that truckload options would be quite valuable to the trucking industry.
This dissertation develops a method to create value through more flexible procurement contracts, which could benefit the trucking industry as a whole – particularly in an uncertain business environment. Truckload rate and truckload options price are solidly investigated and modeled. In addition, parameter estimation for a continuous stochastic model is practically explored using discrete statistics. Finally, numerical trading examples are illustrated and a picture of truckload option trading becoming reality presented. The complicated results indicate that truckload options have the potential of stimulating the entire trucking and logistics industries.
Because of the growing importance of hub-and-spoke operation s in the trucking industry , crossdocking has become an important and effective tool to transfer freight. Companies like Wal -M art , Costco an d Home Depot are using this kind of facility in their logistics operations . Efficiently operating crossdocks, thereby reducing unnecessary waiting and staging congestion for freight and workers is an important issue for managers.
This dissertation uses real-time information about the contents of inbound and outbound pallets and the locations of pallets to schedule unloading for waiting trailers and assign destinations for pallets. We show how to incorporate the information of waiting freight in trailers to benefit trailer scheduling; we also show how to use the information on freight staging to mitigate congestion. Two dynamic trailer scheduling and four alternate destination strategies are proposed and compared with baseline scenarios.
Our simulation results suggest that:
1. Our strategies are effective. The two time-based trailer scheduling algorithms can save cycle times as much as 64%, 57% and 30% in the 4-to-4, 4-to-8 and 8-to-8 crossdock scenarios, respectively; the four alternate destination strategies can save cycle times as much as 34% in the 8-to-8 staging crossdock scenarios. In addition, these strategies can raise throughputs for crossdocks. These effects should result in a noticeable improvement in supply chain networks, including shorter transportation lead-times, more reliable on-time deliveries and lower inventory costs.
2. In our alternate destination strategies, even if a destination change results in extra time for value-added services for freight, the strategies are still worth adopting.
3. The combination models of our trailer scheduling algorithms and alternate destination strategies work better than solely implementing an alternate destination strategy when trailer arrivals are dense.
4. A higher flexibility in choosing alternate destinations can bring higher performance for crossdocks.
Efficient freight transportation is an essential for a strong economic system. A rapid growth of freight demand, however, lessens the efficiency of provided infrastructure. In order to alleviate this problem effectively, evaluation studies have to be performed in order to invest the limited budget for the best of social benefits. In addition to many difficulties on making a decision for each project investment, it is made harder by the complimentary and substitution effects that happen when considering transportation project together. Current practices, however, limit number of project combinations in order to avoid numerous tests. The best project combination may have never been realized.
This dissertation proposes network design models which can automatically create project combinations and searching for the best. The network design models have been studied for the passenger movements and focus on highway expansions. In this dissertation, the focus is shifted to the freight movements which involve multimodal transportation improvements. Our freight network design model is developed based on the bi-level optimization model. The development then involves two components. The first task is to set the freight investment problems within the bi-level format. This includes finding a suitable freight flow prediction model which can work well with the bi-level model. The second task is to provide a solution algorithm to solve the problem.
The dissertation sets the framework of the freight flow network design model, identifies expecting model issues, and provides alternatives that alleviate them. Through a series of developments, the final model uses the shipper-carrier freight equilibrium model to represent freight behaviors. Capacity constraints are used as a mean to emphasize limited services since the reliability issues, an important factor for freight movements, cannot be captured by steady state traffic assignment. A case study is implemented to allocate a budget for improvements on the California highway network. The transportation modes are selected by the shipper model which can be trucks, rails, or the multimodal transportation. The results shown that the proposed network design model provided better solutions compared with traditional ranking methods. The solution algorithm can manage the problem with reasonable project alternatives. However, the computation expense increases rapidly with increasing number of project alternatives.
Commercial vehicles typically represent a small fraction of vehicular traffic on most roadways. However, their influence on the economy, environment, traffic performance, infrastructure, and safety are much more significant than their diminutive numerical presence suggests.
This dissertation describes the development and prototype implementation of a new high-fidelity inductive loop sensor and a ground-breaking commercial vehicle classification system based on the vehicle inductive signatures obtained from this sensor technology. This new sensor technology is relatively easy to install and has the potential to yield reliable and highly detailed vehicle inductive signatures for advanced traffic surveillance applications
The Speed PRofile INterpolation Temporal-Spatial (SPRINTS) transformation model developed in this dissertation improves vehicle signature data quality under adverse traffic conditions where acceleration and deceleration effects can distort inductive vehicle signatures. The axle classification model enables commercial vehicles to be classified accurately by their axle configuration. The body classification models reveal the function and unique impacts of the drive and trailer units of each commercial vehicle.
Together, the results reveal the significant potential of this inductive sensor technology in providing a more comprehensive commercial vehicle data profile based on a unique ability to extract both axle configuration information as well as high fidelity undercarriage profiles within a single sensor technology to provide richer insight on commercial vehicle travel statistics.
The activity-based travel demand model recognizes that travel is derived from the demand for activity participation distributed in space. The focus on intra-household interactions and linkages between people’s behavior and social and physical environment has been identified as emerging features of the activity-based approach that would be important to travel behavior research. The dissertation is dedicated to an in-depth exploration of the within-household interactions by theoretical specification and empirical development of the household activity time allocation models based on a utility maximization framework with the household as the unit of analysis. Furthermore, the dissertation also aims to propose a model of the household activity scheduling process primarily focusing on task allocation mechanisms on the basis of the human agents adjusting themselves to the built social and physical environment.
Development of the activity time allocation model in this dissertation includes two types of structural time allocation models. First, the collective models based on two assumptions that household heads have their own utility functions and that decisions by them reach Pareto-efficient outcomes are introduced to develop intra-household activity time allocation models for leisure demand and housework activity. Secondly, intra-household time allocation to housework activity is further examined through the estimation of time allocation to the different types of activities by the different types of household members along with extensive exploration of various theories and identification of related interactions.
This dissertation proposes a household activity scheduling process a model design based on a weekly pattern system, which is expected to keep various advantages compared to a deterministic daily model system. Along with learning and adaptation procedures, the human being as a learning agent is designed to prepare strategic plans of behavior to achieve individual goals through interactive environments, and operationalize those plans via activity execution requiring the participation of other agents. At the household level, the household and its members as decision agents are also designed to optimize the allocation of the available household labor resource under the presence of the uncertainties of the physical and social environments. After describing the mathematical framework and solution procedure, a simulation experiment is conducted within a hypothetical environment to demonstrate how the proposed model works.
Estimating and forecasting travel demand have been a popular study topic among transportation researchers; however the research needs to pursue new direction with the advent of data from the potential availability of newer types of data previously not envisaged. In this dissertation, the author develops approaches for two aspects of travel demand analysis in the transportation network: A newer OD estimation method, and a household activity-based demand modeling framework.
First, a trip-based dynamic OD estimation model is developed. Several previous studies on OD trip table estimation focused on a static problem and many recent dynamic OD estimation methods also have not sufficiently proved their practical applicability. In order to overcome the shortcomings, this dissertation introduces supplementary information (i.e., vehicle trajectory data) to a dynamic OD estimation model.
However, the trip-based approach has certain well-known limitations. OD estimation results can not give satisfactory solutions for forecasting purposes, and the estimated OD table only contains materialized trips, which implies that no latent travel demand is included in the table. To overcome these drawbacks, the second item of focus in the dissertation is in developing a dynamic agent-based household activity and travel demand simulation model framework named DYNAHAP. The framework calculates a demand pattern in terms of activity chains generated by synthetic families. A traffic simulator then executes the activity chains, and finally an aggregated dynamic traffic pattern is generated.
In order to calibrate DYNAHAP, various activity data should be gathered. Such tasks had been regarded very difficult or even nearly impossible before, but with the development of data collecting technologies, currently we have several ways for collecting the activity chains of individuals. Like vehicle trajectory data, sample activity chains collected from personal communication devices such as PDA (Personal digital assistant) could be used for DYNAHAP calibration. Some numerical test results also will be given for the purpose of proving the performance of the developed models.
The objective of this dissertation is to develop a decision-making method framework for prioritizing various potential alternatives of truck management strategies using Multi-Criteria Decision-Making (MCDM) method. The motivation of this research is derived from the need of investigating and evaluating all likely impacts resulting from the implementation of truck strategies. Since the conventional evaluation methods such as the cost-benefit analysis can only be considered impacts involving monetary scales, we believe these are insufficient to investigate the all likely impacts. Our method is developed in order to address all measures that can transformable and non-transformable as well as to reflect decision-makers’ priorities of the problem. As a result, two main objectives are accomplished in our study. The first is to investigate the all likely impacts resulting from the implementation of truck management strategies by performing a specific case study of before and after cases using traffic simulation models. A key feature of this part is to analyze various performance measures. They include both measures that can transformable and non-transformable into monetary costs as well as can reflect the standpoints of the public and the private sectors. Secondly, a decision-making method is developed using the Analytical Hierarchy Process (AHP) method which is one of popular multi-criteria decision-making (MCDM) methods. This method enables the judgments and preferences of decision-makers to be quantified based on the relative importance of their own criteria, and to allow a quantitative interpretation from others. Another important contribution of our work is to suggest a “score-allocation” method which is a normalization technique. Since quantitative measurements have different scales, we need to incorporate these measurements into a single value. This method allows decision-makers easily to facilitate comparisons among potential alternatives. We believe that scores across alternatives provide the argument to prioritize potential alternatives of truck strategies.
The objective of this dissertation is to develop and apply an analytic procedure that estimates the amount of traffic congestion (vehicle hours of delay) that is caused by different types of accidents that occur on urban freeways, as well as to develop a model for prediction of real-time accident information such as how long an accident will affect traffic congestion and to the extent of the traffic congestion. Although it has been speculated that non-recurrent congestion caused by accidents, disabled vehicles, spills, weather events, and visual distractions accounts for one-half to three-fourths of the total congestion on metropolitan freeways, there are insufficient data to either confirm or deny this conjecture.
The first part of this dissertation develops a method to separate the non-recurrent delay from any recurrent delay that is present on the road at the time and place of a reported accident, in order to estimate the contribution of non-recurrent delay caused by the specific accident. The procedure provides a foundation for a forecasting model that will assist transportation agencies such as Caltrans to allocate resources in the most effective way to mitigate the effects of those accidents that are likely to result in the greatest amount of delay. Additionally, since freeway travelers may be able to alter their driving routes based on the real-time accident information, the forecasting model may reduce traffic congestion and the incidence of secondary accidents.
Since a number of estimated delay results were censored by time and/or space boundary conditions, general statistical approaches were not available. An approach based on survival analysis was applied to analyze estimated delay and to predict traffic congestion impact in terms of time and space. Specifically, a statistical model based on the Cox type proportional hazard analysis is estimated that describes non-recurrent delay as a function of day of week, time of day, weather, and observable (e.g., from emergency calls and/or aerial or on-scene observation) characteristics of the accident. These accident characteristics, which are available to Freeway Traffic Management Systems, include time of day, number of involved vehicles, whether a truck is involved, and collision location (by lane or side of road). This statistical model can be used to inform a manager as to the expected delay associated with an accident as soon as the accident is reported and its characteristics are observed. This can in turn be used in improving resource allocation.
Additionally, this dissertation develops three prediction models regarding the spatio-temporal impact caused by a traffic accident as well as an accident duration model based on AFT metric model. Information provided by such predictions can play an important role in public sector transportation agencies providing freeway travelers with real-time traffic information under incident conditions.
The so-called activity-based approach to analysis of human interaction within social and physical environments dates back to the original time-space geography works of Hägerstrand and his colleagues at the Lund School in 1970, with a unique kernel problem termed “household activity scheduling”. The problem attempts to derive estimates of activity decisions taking into account the time, duration, mode, location and route of the given activity sets performed by individuals.
This dissertation research studies the activity scheduling/rescheduling problem under an uncertain environment. Theories and models for predicting activity-travel behavior are developed within the context of an activity-based approach built on the general consensus that the demand for travel is derived from a need or desire to participate in activities. Computationally-tractable systems are developed that inherently incorporate factors of uncertainty that can potentially increase the ability to address the household activity scheduling problem and the related dynamics of human movement required for social interaction and household sustenance. A stochastic mixed integer linear program is formulated to model travel behavior in which each activity of the prescribed household agenda has a known probability of being completed (or cancelled). Further, a chance-constrained program is proposed to determine the optimal activity/travel pattern when travel time and activity duration are assumed to be stochastically distributed, while the remaining inputs are precisely known. Finally, under the assumption that the activity/travel pattern involves a dynamic decision-making process of rescheduling/adaptations to initial plans subject to unexpected events, a predictive model of activity rescheduling behavior is developed in the form of a mixed integer linear program.
The dissertation presents solution methodologies to the proposed models. Data drawn from a comprehensive on-line survey are utilized to verify the proposed activity schedule/reschedule models. Numerical results are presented to demonstrate the performance of the proposed models. Finally, conclusions and directions for future research are summarized.
Traffic operations field computational resources as well as the bandwidth of field communication links are often quite limited. Accordingly, for real-time implementation of Advanced Transportation Management and Information Systems (ATMIS) strategies, such as vehicle re-identification, there is strong interest in development of field-based techniques and models that can perform satisfactorily while minimizing field computational and communication requirements. The ILD (Inductive Loop Detector)-based Vehicle ReIDentification system (ILD-VReID) is an example of a currently applied approach. Although ILDs are not without limitations as a traffic sensor, they are widely used for historical reasons and the sunken investment in the large installed base makes their use in this research highly cost-effective. Therefore, this dissertation develops a new vehicle re-identif
ication algorithm, RTREID-2, for real-time implementation by adopting a PSR (Piecewise Slope Rate) approach that extracts features from raw vehicle signature data. The results of cases studies indicate that RTREID-2 is capable of accurately providing individual vehicle tracking information and performance measurements such as travel time and speed. The potential contributions of RTREID-2 are: application to square and round single loop configurations, and reduced computational requirements associated with re-estimation or transferability of the speed models used in the previously developed approach. As a consequence RTREID-2 is obviated for site-specific calibration and transferability issues. A freeway corridor study also demonstrates that RTREID-2 has the potential to be implemented successfully in a congested freeway corridor, utilizing data obtained from both homogenous and heterogeneous loop detection systems. A real-time vehicle classification model, which is based on the PSR approach, was also developed on the part of RTREID-2. The classification model can successfully classify vehicles into 15 classes using single loop detector data without any axle explicit information. The initial results also suggest the potential for transferability of the vehicle classification approach and are very encouraging. To investigate real-time freeway performance measurement in a real-world setting, the design of RTPMS (Real-time Traffic Performance Measurement System) that is based on RTREID-2 is also presented in this dissertation. A simulation of RTPMS is conducted to evaluate its feasibility. The simulation results demonstrate the potential of implementing RTPMS in real world application.
Kalandiyur, Nesamani. Estimating Vehicle Emissions in Transportation Planning Incorporating the Effect of Network Characteristics on Driving Patterns. PhD, Transportation Science, 2007. 189 pp. Advisers: R. Jayakrishnan and Michael G. McNally
Variations in traffic volumes and changes in travel-related characteristics significantly contribute to the level of vehicular emissions. However, in current practice, travel forecasting models rely on steady state hourlyaverages and are thus incapable of accurately capturing the effects of network traffic variations accurately on emissions. Recent research has focused on the implementation of modal emission models to overcome some of these shortcomings in existing emission rate models. A primary input to modal emission models is the fraction of time spent in different driving patterns. The estimation accuracy, however, is hampered by the application of static travel demand models for predicting driving patterns. There is a real need to evolve alternate methods to accurately
predict driving patterns.
This dissertation proposes an approach to predicting driving patterns more accurately by applying different models at the macroscopic and microscopic network levels. The proposed models more accurately estimate the driving pattern by considering a set of Emission Specific Characteristics (ESC) for each network link. Specific ESC considered in this research includes geometric design elements, traffic characteristics, roadside environment characteristics, and driver behavior.
Two different models have been developed in this study to capture the driving patterns at each network level. The first model is designed to capture macro-scale driving patterns (average speed) in a larger network and the second model is designed to capture micro-scale driving patterns. The two models have been developed using structural equations. They have been calibrated, evaluated, and validated using a microscopic traffic simulation model. Analysis of the models reveals that geometric design elements exert greater influence on driving patterns than traffic characteristics, roadway environment characteristics, and driver behavior in the estimation of emissions. This research has concluded that, for congested traffic conditions, the proposed models capture driving patterns more accurately than current practice and, consequently, these models estimate the range of emissions more accurately. Models that estimate time-dependent emissions in the presence of traffic sensor data were also successfully
The aviation industry has sought to address the negative externality of aircraft noise using a variety of approaches, but there has been little theoretical work to date encompassing both the market implications and the social optimality of air transportation noise policy. This dissertation develops simple theoretical models to analyze the effects of noise regulation on an airline’s scheduling, aircraft ‘quietness’, and airfare choices. Monopolistic and duopolistic airline competition are modelled, and two types of noise limits are considered: maximum cumulative noise from aircraft operations and noise per aircraft operation. As expected, tighter noise limits, which reduce community exposure to noise, also cause airlines to reduce service frequency and raise fares, which hurts consumers. Welfare analysis investigates the social optimality of noise regulation, taking into account the social cost of exposing airport communities to noise damage, as well as consumer surplus and airline profit. Numerical simulations show that the type of noise limit has a significant effect on the magnitude of the first-best and second-best optimal solutions for service frequency, cumulative noise, and aircraft size and level of quietness. Furthermore, the numerical analyses suggest that under the more realistic second-best case, the cumulative noise limit might be a preferable policy instrument over the per-aircraft noise limit. In the monopoly’s parameter space exploration, welfare is found to be slightly higher, cumulative noise is lower, and the fare is slightly lower when the planner controls cumulative noise rather than per-aircraft noise. In the duopoly case, when the per-aircraft limit yields greater welfare than the cumulative limit, the per-aircraft limit offers only modest welfare gains above the levels achieved with the cumulative limit. But when the cumulative limit yields greater welfare than the per-aircraft limit, the cumulative limit offers substantial welfare gains above the levels achieved with the per-aircraft limit. The effects of noise taxation and the optimal level of noise taxes are also investigated with the duopoly model; the analysis shows equivalence between noise taxation and the cumulative noise limit.
Within the existing body of activity scheduling behavior models, the Household Activity Pattern Problem (HAPP) model is an activity-based model characterized by a rigorous mathematical programming formulation. The HAPP model can deal with detailed activity patterns including spatial, temporal, personal and modal information with complex constraints. The HAPP model is in the form of a Mixed Integer Programming model (MIP) which includes both continuous variables and discrete variables. Such temporal attributes of an activity pattern as starting time, duration and ending time are continuous variables, and those spatial attributes associated with the sequencing of activities, travel modes, participation persons and vehicles are discrete variables.
As formulated, the HAPP model is a constrained utility maximizing model. Empirical application of the model to a demand context involves estimation of the components of the objective function, based on data from observed patterns. However, due to computational difficulties in HAPP model, genetic algorithms (GA) have been proposed to estimate the set of factors influencing the objective function that "best" reproduces the observed spatial and temporal interrelationships. The fitness score in the GA approach used to evaluate the quality of the representation is the difference between the observed activity pattern scheduling (OAPS) and predicted activity pattern scheduling (PAPS), or the similarity between the two
In this dissertation, we propose a new similarity metric for the GA estimation procedure. The metric considers the problem based on the continuous representation of discrete activity variables along the temporal dimension. Three similarity judging rules work together to form the similarity definition of similarity metric. They are: the temporal overlap among activities of different type, correspondence between participant person and vehicle used for each activity; permutations in the temporal sequence of activities and activity duration length similarity. The estimation procedure is tested on data drawn from a well-know activity/travel survey.
Current travel forecasting models have had limited sensitivity to policy decisions. One of the primary challenges with travel forecasting models (both experimental and those implemented) is limitations in the data. The primary data source, the daily travel diary, is limited in both accuracy and sample size. The daily travel diary has known problems with underreporting, time inaccuracies, respondent fatigue, and other human errors. Global positioning systems (GPS) have been recently used to supplement the daily travel diary. As GPS becomes more accurate, reliable, and cost effective, could it entirely replace the daily travel diary?
A number of efforts have used GPS data for route choice studies and to supplement daily travel diaries by providing more accurate time data, and determining under-reporting rates. GPS is also used in computer assisted daily travel diaries, reminding respondents of activities they may have forgotten to report.
GPS devices record times and locations of each activity and the trips between those activities. To use GPS data to replace the daily travel diary one need only predict the activity types. The goal of this research is to develop and test a model to predict activity types based solely on:
This thesis summarizes models developed using discriminant analysis and classification/ regression trees. The models predicted in which of 26 different activity types the individual participated. Accuracy for out of home activities for the best model was 63%. When combed with the activity of being at home (which can be accurately predicted if we know the individuals home location) an accuracy of 79% was achieved (72% if you consider that GPS data may miss as much as 10% of trips). Since travel diaries have been known to underreport trips by as much as 25%, GPS data with the model developed can be very competitive. It is even more appealing considering the time inaccuracies and human error associated with travel diaries.
Auction based market clearing mechanisms are widely accepted for conducting business-to-business transactions. This dissertation focuses on the development of auction mechanism decision tools for freight transportation contract procurement in spot markets and long-term markets. Spot markets have found their niche because of the Internet and standard classic auctions are widely employed. For long-term markets, large shippers (typically manufacturing companies or retailers) have begun to use combinatorial auctions to procure services from trucking companies and logistics services providers. Combinatorial auctions involve very difficult optimization problems both for shippers and carriers. In the US truckload market very few carriers have the technical know-how to bid in combinatorial auctions. To reduce these problems we look at a different auction scheme termed a unit auction, where the shipper can exploit the economies of scope in the network and give the carriers the chance to bid on pre-defined packages similar to 'lotting' in supply chain procurement. Shippers have non-price business constraints, which must be included in the winner determination problems to closely match shipper business objectives. We develop allocation formulations incorporating the non-price business constraints and Lagrangian based heuristics for solving them in both unit auctions and combinatorial auctions. We provide carrier bidding framework for classic auctions in spot markets using concepts from economic auction theory. For bidding in combinatorial auctions, we study the effects of demand uncertainty, carrier network synergies and strategic pricing, and shipper's winner determination problems on carrier bidding using optimization-based simulation analysis. We also provide a framework for volume-based contracts using insights from classical transportation problem.
Further, we also present a mechanism for cross shipper auctions for shipper collaboration and alleviate logistical inefficiencies like deadheading and dwell times for carriers. Finally we develop pareto efficient profit sharing mechanisms among shippers using co-operative game theory.
This dissertation defines and studies a class of dynamic problems called the “Mass Transport Vehicle Routing Problem” (MTVRP) which is to efficiently route n vehicles in real time in a fast varying environment to pickup and deliver m passengers, where both n and m are large. The problem is very relevant to future transportation options involving large scale real-time routing of shared-ride fleet transit vehicles. Traditionally, dynamic routing solutions were found using static approximations for smaller-scale problems or using local heuristics for the larger-scale ones. Generally heuristics used for these types of problems do not consider global optimality.
The main contribution of this research is the development of a hierarchical methodology to solve MTVRP in three stages which seeks global optimality. The first stage simplifies the network through an aggregated representation, which retains the main characteristics of the actual network and represents the transportation network realistically. The second stage solves a simplified static problem, called “Mass Transport Network Design Problem” (MTNDP). The output of stage 2 is a set of frequencies and paths used as an initial solution to the last stage of the process, called Local Mass Transport Vehicle Routing Problem (LMTVRP), where a local routing is performed.
The thesis presents the proposed methodology, gives insights on each of the proposed stages, develops a general framework to use the proposed methodology to solve any VRP and presents an application through microsimulation for the city of Barcelona in Spain.
Since the early 1990’s, public policies for transportation planning have evolved towards modally balanced transportation systems, requiring planning agencies to more precisely evaluate the capacity of their transportation systems, considering all feasible modes as well as low-cost capacity improvements. However, existing methods for capacity analysis are limited to either an individual facility or a single mode network, and thus appear insufficient for multimodal systems capacity analysis. This dissertation presents an advanced method for capacity assessment that can serve as an analytical tool for strategic planning of freight transportation systems, particularly from a multimodal perspective. The multimodal network capacity model proposed in this research takes a mathematical form of a nonlinear bi-level optimization problem with an embedded user equilibrium network assignment problem at its lower level. The bi-level problem, referred to as the MNCP model in this thesis, is comprehensive in the sense that many crucial factors are incorporated including multiple modes and commodities, behavioral aspects of network users, external factors, as well as the physical and operational conditions of a network. The numerical tests designed to illustrate the application of the proposed MNCP model indicate that the algorithm developed for solving the bi-level problem has been successfully implemented. These results show the capability of the model not only to estimate the capacity of a multimodal network, but also to identify the capacity gaps over all individual facilities in the network, including intermodal facilities. By incorporating more precise capacity measures into the planning process, planning agencies would benefit from the MNCP model in articulating investment priorities across all transportation modes, thus achieving their goal of developing sustainable transportation systems in a cost-effective manner.
Advances in traffic surveillance technology can provide more complete and intelligent data from detectors. This dissertation describes an improved method of freeway performance measurement that integrates multi-sensor data fusion with a vehicle-monitoring algorithm capable of identifying the same vehicle/s at different locations. To obtain a more robust and effective data set for vehicle monitoring, data fusion from two state–of–the-art traffic detectors -- loop detectors and video detectors -- was introduced. Investigations and development of a new algorithm for data fusion and real-time vehicle monitoring - TRASURF (TRAffic SURveillance and perFormance) were also described. The algorithm’s development was based on an examination of feature vector extraction from each advanced traffic sensor, data fusion across multiple technologies and analysis of sensor performance. A real-world data set from one section of the I-405 freeway was applied to develop and evaluate the algorithm for a single freeway section. Based on extensive analysis of these field data, the PARAMICS (PARAllel MICroscopic Simulation) model was used to generate simulated fused data. This simulation served as the means to test and evaluate the performance of TRASURF as a multi-section vehicle-monitoring algorithm. The algorithm’s ability to reconstruct individual vehicle trajectories will enable more efficient and effective traffic surveillance, and will enhance the collection and analysis of network-wide traffic information including path travel time and origin-destination matrices. Furthermore, investigations and descriptions of various applications of advanced detectors for traffic analysis, especially in the context of the single-loop configuration widely used within California and many other locations were made. Traffic data extraction based on advanced loop detectors will make a vital contribution to many aspects of traffic operations and management, as these data are not available from conventional detectors.
Urban motorists impose social externalities through accidents, travel delay and environmental damage. There is little question about the importance of these impacts but widespread debate about their costs. This dissertation proposes improvements to the ways in which the external costs of urban road use are characterized, modeled and estimated. I first suggest that the conventional approach of modeling accident externalities is likely to understate their magnitude. I construct a theoretical framework that characterizes accident and travel delay costs with explicit components for physical risk, travel delays, and the defensive efforts that link them and develop an empirical model from this framework to estimate accident and travel-delay costs both jointly and separately. My results suggest that external accident costs represent 44% of the overall externalities generated during a typical peak-period commute -- far higher than estimates from more conventional modeling approaches. I then use these results to analyze the related issues of travel delay and "value of time". Travel delays represent the bulk of the externalities faced by motorists during peak commute periods. I use empirical data from a toll-pricing project on Interstate 15 in San Diego, California, to address discrepancies in value of time estimates generated by previous revealed/stated-preference studies. Using Rubin's Multiple Imputation Methodology, I find that the median commuter’s “value of time” is $30 per hour, which is consistent with the range of estimates reported by related congestion-pricing studies. But I also find that the option for faster travel has different values for different commuters. My median “value of time” estimates range from $7 per hour for low-income, part-time workers making non-work trips, to $65 per hour for high-income, full-time workers on their daily commutes.
Cortes, Cristian Eduardo. High Coverage Point to Point Transit (HCPPT): A New Design Concept and Simulation-Evaluation of Operational Schemes. Ph.D., Civil Engineering, 2003. 414 pp. Adviser: R. Jayakrishnan.
This dissertation research proposes the development and evaluation of a new concept for high-coverage point-to-point transit systems (HCPPT). Overall, three major contributions can be identified as the core of this research: the proposed scheme design, the development of sophisticated routing rules that can be updated in real-time to implement and optimize the operation of such a design, and the implementation of a multi-purpose simulation platform in order to simulate and evaluate such a design under real network conditions. The design is based on Shuttle-style operations with a large number of deployed vehicles under a coordinated transit system that uses advanced information supply schemes with fast routing and optimization schemes. The system design is rather innovative and ensures that no more than one transfer is needed for the travelers, by using transfer hubs as well as reroutable and non-reroutable portions in the vehicles' travel plans. It yields flexibility for demand-side benefits from options such as price incentives for time-bound "passenger-pooling" at the stops without destination constraints, by the users. A strict optimization formulation and solution for such a problem is computationally prohibitive in real-time. The design proposed in this dissertation is effectively geared towards a decomposed solution using detailed rules for achieving vehicle selection and route planning. If real-time update of probabilities based upon modeling the future dispatch decisions is included, then this scheme can be considered as a form of quasi-optimal predictive-adaptive control problem. Finally, a multi-purpose simulation platform is developed as part of this research in order to evaluate the performance of the system. The final simulations of HCPPT required point-to-point vehicle simulation, which is not possible with off-the-shelf simulators. The simulation framework uses a well-known microscopic traffic simulator that was significantly modified for demand-responsive vehicle movements and passenger tracking. A simulated case study in Orange County showed that with enough deployed vehicles, the system can be substantially better, even competitive with personal auto travel, compared to the often-unsuccessful traditional DRT systems and the existing fixed route public transit. Furthermore, HCPPT can be incrementally implemented by contracting out services to existing private operators.
McMillan, Tracy Elizabeth. Walking and Urban Form: Modeling and Testing Parental Decisions about Children's Travel. Ph.D., Urban & Regional Planning, 2003. 156 pp. Advisers: Marlon G. Boarnet and Kristen M. Day.
Over the past several years, the private vehicle has become the predominant mode of travel to school while walking and bicycling rates have decreased. Some suggest that this change in travel behavior contributes to negative health outcomes in children, including increased rates of (1) overweight/obesity through inactivity and (2) pedestrian and bicyclist fatality and injury. A series of recent policies and programs directly attribute the change in travel behavior to school to the urban form of communities. Limited research exists to support this hypothesis, however. The fundamental questions of whether and how urban form impacts a child's trip to school must to be answered in order to develop effective interventions aimed at increasing rates of walking and bicycling activity and safety. This research proposes a conceptual framework to examine the nature and shape of the relationships between urban form; interpersonal, demographic and social/cultural factors; parental decision-making and a child's travel to school. Using parent survey data on children's travel to school and urban design assessments from twelve elementary school neighborhoods, the relative influence of urban form on the mode choice to school was first determined. Results indicate that urban form elements such as street lights and street widths do affect the probability of a child walking or bicycling to school; however, the affect of these elements is modest compared to other influential variables such as the perceived convenience of driving, country of birth, family support of walking behavior, reported traffic conditions in the neighborhood and perceived distances between home and school. A second analysis examined how urban form and children's travel behavior relate by testing the hypothesis of an indirect relationship. The findings show that parent's feelings of neighborhood safety, traffic safety and/or household transportation options do not intervene in the relationship between urban form and children's travel behavior. Socio-demographic characteristics and parent's attitudes toward travel, however, may modify the strength of the relationship between urban form and children's travel behavior. The results of this study advance the discussion on relationships between urban form, transportation and health and inform policy and practice of the best targets for future planning interventions.
One of the fundamental requirements to facilitate implementation of any advanced transportation management and information system (ATMIS) is the development of a real-time traffic surveillance system to produce reliable and accurate traffic performance measures. This dissertation presents a new framework for anonymous vehicle tracking that is capable of tracing individual vehicles by utilizing vehicle features. The core part of the proposed vehicle tracking method is a vehicle reidentification algorithm for signalized intersections based on inductive vehicle signatures, which consists of two major components: search space reduction and probabilistic pattern recognition. Both real-time intersection performance and intersection origin-destination (OD) information can be obtained as basic outputs of the algorithm. An evaluation framework for vehicle tracking performance using a microscopic traffic simulation model was developed. A systematic simulation investigation of the performance and feasibility of anonymous vehicle tracking across multiple detector stations using the proposed simulation evaluation framework was conducted. The proposed anonymous vehicle tracking system produces a rich data source for accomplishing OD estimation, which is explored in this dissertation. Additional useful applications of inductive vehicle signatures are also presented. These include the development of a methodology for evaluating traffic safety based on individual vehicle information and the prediction of section travel times via the vehicle reidentification technique. The proposed anonymous vehicle tracking methodology could be an invaluable tool for operating agencies in support of numerous intelligent transportation systems (ITS) strategies including congestion monitoring, adaptive traffic control, system evaluation, and provision of real-time traveler information.
A review of the literature reveals that formulating an optimal signal control problem for surface street networks presents difficulties associated both with its modeling and its solution. The consistent modeling of the traffic flow process as a linear model necessitates the mathematical representation of some type of conditional piece-wise functions that describe the flow at lattice points on the surface street network depending on the prevailing traffic conditions and the signal indication. Representing such complex non-linear functions by a linear model is a non-trivial task. Based on analogies from the theory of mathematical logic we developed two methodologies for transforming such functions into a Mixed Integer Model (MIM) that is an equivalent representation corresponding to a set of linear equations and/or inequalities. The methodologies can be applied either towards the development of MIM representations or for the analysis of the structure of existing representations. Specifically, in this dissertation we develop MIM representations for virtually every possible piece-wise conditional function that can be found when developing a model for a surface street network based on the widely used dispersion-and-store or the cell transmission traffic flow models; further, we analyze and provide an improved MIM for the piece-wise conditional function that describes the flow according to the cell transmission model. The consistent modeling of the control strategy necessitates the consideration of a dual ring, 8-phase, variable cycle controller. For this we develop a model for the control strategy based on the aforementioned controller type, in contrast to all previous approaches in which a fixed cycle, 2-phase controller is considered. The linear optimal control problem is solved as a large scale Mixed Integer Linear Programming problem. It is known from the theoretical findings of optimal control and optimization theory that this type of problem is particularly difficult to solve. A number of optimal signal control problem variations are solved for an isolated intersection that accommodates eight movements during an optimization horizon of 5 minutes, by a commercial solver that uses a branch-and-cut algorithm. The solution time for all variations of these problems were faster than real time; however, an optimization horizon of 10 minutes required a solution time significantly slower than real time, ostensibly because the system states increased dramatically. We propose a logic-based formulation for the control strategy model that can be used for the development of a specially-tailored branch-and-bound algorithm for the problem of optimal signal control. We believe that a combination of the branch-and-cut with the customized branch-and-bound algorithm could efficiently solve the optimal signal control problem for high order systems. Finally, the solutions prove the effectiveness, adaptability, and versatility of the control strategy that is based on the concept of a dual ring, 8-phase, variable cycle controller, as well as the quality, of the decisions ordered by solving an optimal signal control problem.
The activity-based approach to travel demand analysis recognizes that human activities dictate travel. Microsimulation of household activity patterns has gained significant attention as a method for modeling this activity participation. Existing approaches, however, focus on modeling how households solve the activity scheduling problem - how and when each household member should engage in particular activities to meet the needs of the household. This is a top-down approach that recognizes inherent causal links between members of a household but sacrifices modeling flexibility that is necessary for complex policy analysis. This dissertation synthesizes dominant activity analysis theories with concepts from the social simulation and complex systems analysis literature to demonstrate that the motivation and constraints that shape activities are more directly embodied in the activity execution problem - how individuals interact with other entities in their environment to engage in activity. The scheduling problem is re-cast as the adaptive internal process that an individual uses to navigate through this interactive environment to achieve environmentally-derived payoffs. Based on this theory, a microsimulation is described that focuses on the activity execution process. Such a bottom-up approach presents a problem of tractability. This dissertation solves this problem by describing activity execution using a model of negotiated interaction derived from the Contract Net Protocol for distributed computation. This model is shown to be tractable in terms of the number of negotiating individuals, given reasonable limitations on the negotiation process. Then, a complete agent-based model of an urban activity system is described based on this activity execution kernel. This general model is shown to be tractable in terms of the population size, given assumptions on how negotiations are initiated. Finally, results from experiments using candidate adaptive learning algorithms for agents operating in the microsimulation are presented to demonstrate the utility of the microsimulation approach.
This dissertation focuses on the development of optimization methods and approximation algorithms for combinatorial auctions, particularly with application to the contract procurement problem in freight transportation. Combinatorial auctions are auctions in which a set of heterogeneous items are sold simultaneously and in which bidders can bid for their preferred combinations of items. They involve many difficult optimization problems both for auction hosts and bidders and have received significant attention from computer scientists, operations researchers and economists recently. Large shippers (typically manufacturing companies or retailers) have begun to use this method to procure services from trucking companies and logistics services providers. This dissertation first analyzes the economic impact of combinatorial auction-based procurement methods both on shippers and carriers using a simulation study and reveals that both parties can benefit from this economically efficient price discovery mechanism. While the majority of prior research has been from an auctioneer's perspective, we demonstrate that bidders have even more complicated optimization problems in combinatorial auctions. The bid construction problem, that is, how bidders should identify and construct beneficial bids, is very hard and remains an open question. This dissertation investigates this problem and proposes an optimization based approximation method that involves solving an NP-hard problem only once, yielding significant improvements in computational efficiency. Further, the current state of trucking and third party logistics industries are examined. The trucking industry is very competitive and small carriers are operating under thin margins. This dissertation addresses these issues by proposing an auction based collaborative carrier network in which participating carriers can identify inefficient lanes from daily operations quickly and exchange them with partners under an auction protocol. This system is proved to be Pareto efficient. Further, decision problems are discussed regarding how carriers should identify inefficient operations and how to make and select bids. This represents an effort to use advanced auction mechanisms to enhance the carriers' operational efficiencies.
Hypercongestion gives the problem of the non-unique relationship between travel time and flow in the fundamental diagram of traffic flow, which depicts the relationship between flow and density. Under the assumption of an exogenous time-pattern of demand and with the hypercongestion model in Small-Chu (1997), chapter one develops the backward iterative method in Vickrey (1991) to derive the marginal cost of additional entries at different times. The results show that the magnitude of the marginal external cost depends on not only the exogenously given entry rate but also the length of the entry period. With exogenous time pattern for demand, the marginal external cost of additional entry in the transportation system will increase to a peak from the beginning. Then it will decrease. During the entry period, the marginal cost curve is approximately symmetric. We can use policies, for example, staggering the work starting time, controlling the number of entry to change the entry time pattern to relieve the congestion. It has been noted that there is vicious cycle or virtuous cycle in production of transit services. However, few empirical researches have been done on transit service with consideration of this dynamic simultaneity. In chapter two, I will use dynamic simultaneous equations to model the dynamic simultaneous relationship between transit demand, transit supply and transit cost structure. The results show strong inertia in the bus demand, supply and cost. The response of supply level to the change of demand is consistent to the square root rule (Mohring 1972). This simultaneous model found much stronger scale economy in bus transit, both in short run and long run. The policy simulations show that the higher bus fare will decrease the ridership of bus at the very beginning. However, later on, the higher service brought by the higher revenue will offset the negative effect on ridership from higher bus fare. The operating deficit will decrease when higher bus fare is charged. Even though the favorable city characteristics could increase bus ridership and decrease the operating deficit at the same time, they are out of bus firm's control.
This dissertation focuses on an initial feasibility study of a self-organizing, distributed traffic information system, called "Autonet," that is based upon peer-to-peer information exchange among vehicles with inter-vehicle communication equipment. Autonet, a concept proposed within the Cal-(IT)2 Transportation Layer of the University of California, Irvine, is defined as an autonomous, self-organizing information network and control system for effective management of interactions among intelligently informed vehicles, roadways, and stations. Before the proposed Autonet system can be implemented in a real-world transportation system, hardware and software requirements need to be identified; ideally, based both on predictions provided by mathematical formulations as well as testing with microscopic traffic simulations. The research in this dissertation focuses on the traffic aspects of the proposed Autonet, using simulation approaches both to assess the potential benefits that might be accrued by the traffic system, and also to evaluate the ability of the computing overlay to handle the traffic "application" which is the first application for the distributed computing network envisioned within the Autonet concept. An existing microscopic traffic simulator, which is treated only as the vehicle mover, is selected and integrated with originally developed inter-vehicle communication modules through application programming interfaces to build the simulation framework for the feasibility analysis of the proposed Autonet system. Traffic-related information propagation in the traffic network via inter-vehicle communication, which is the foundation for the proposed self-organizing, distributed traffic information system, can be tested in detailed modeling under that simulation framework. This dissertation investigates traffic information propagation both in one-dimensional highway/freeway networks including one-direction and two-direction cases, and in two-dimensional arterial street networks, considering various roadway formats and incident conditions, for different combinations of modeling parameters related to the proposed systems. Further, a series of vehicle re-routing applications under the incident condition based upon the proposed self-organizing, distributed information system are tested via the simulation method. Analysis of the simulation results is given for individual groups of vehicles and for the whole system to find potential benefits from Autonet implementation. Finally, this dissertation identifies needs for future research both for the modeling effort and for some issues involving actual implementation.
Chalermpong, Saksith. Economic Spillovers of Highway Investment: A Case Study of the Employment Impacts of Interstate 105 in Los Angeles County. Ph.D., Urban & Regional Planning, 2002. 139 pp. Adviser: Marlon G. Boarnet.
Most economists agree that new investments in highways at this point in time in the United States have little impact on overall growth in output. New highways play a more important role in shifting economic activities among places, drawing jobs from other locations into the highway corridors, a phenomenon known as negative spillovers. The objective of this dissertation is two-fold, to examine the proposal to decentralize highway finance, which aims to solve the financial responsibility mismatch problem that stems from economic spillovers of highways, and to test the hypothesis of economic spillovers of highway investment at the metropolitan level. First, to better understand how spillovers influence the highway investment decision, the theoretical framework from the interjurisdictional tax competition literature is borrowed to model governments' investment behaviors. Numerical simulations show that decentralized local governments, which independently maximize output in their own jurisdiction, may engage in wasteful investments in highways with the presence of spillovers. Second, to shed more light on the spatial detail of economic spillovers, empirical tests of the spillover hypothesis are conducted at the metropolitan level, with census tracts as the unit of observation. The results of the quasi-experiment reveal census tract employment growth patterns that confirm the existence of negative spillovers caused by the opening of the Interstate 105 in 1993. The benefiting area, which grew substantially after the highway was opened, is limited to a long narrow corridor around the highway, while nearby locations outside the corridor experienced slow growth relative to the rest of the metropolitan area after controlling for various factors. Together, these results suggest that although negative spillovers are present at the metropolitan level, decentralizing highway finance may not be an effective policy to deal with the financial responsibility mismatch problem. Highway finance should remain centralized within metropolitan areas, and regional governing bodies should pay special attention to the distributional impact of highway projects.
Activity-based approaches are perhaps the most promising alternative to the current travel forecasting methodology. This dissertation first presents a pattern generation model that can serve as a link between activity and trip-based methodologies. The model uses a clustering approach to identify groups of similar activity-travel behavior and relates them to household socioeconomic attributes. Minimally, the pattern generation model is offered as a possible replacement to the standard trip generation model. This initial model is then expanded to serve as the core component of a proposed activity-based microsimulation model that constructs complete origin-destination tables using a wholly activity-based approach. The techniques developed provide due diligence to the complex nature of activity-travel behavior in terms of spatial and temporal constraints, household interactions, and the derived nature of such behavior. A successful application of the expanded model is outlined using data from the 1994 Portland activity-travel survey.
One of the main barriers to a better understanding of activities and travel patterns is the difficulty in collecting long-duration data. Previous studies have examined computer-aided interview techniques. Others have researched the potential for global positioning system (GPS) antennas to collect more accurate travel data. This dissertation combines these two techniques by adding the use of wireless communications technology to integrate streaming, real-time GPS data with a dynamically generated, web-based activity survey. In addition, three separate analysis techniques are examined using the results of an informal pilot test. The purpose of these analysis techniques is to weave together the large set of GPS data that can be collected with the much smaller set of activity responses that can be expected. The net result represents both an advance in data collection techniques, as well as a new, peer-to-peer approach to gathering and sharing experiential transportation information, an approach that should be incorporated into future Intelligent Transportation Systems designs.
Yan, Jia. Heterogeneity in Motorists‚ Preferences for Travel Time and Time Reliability: Empirical Finding from Multiple Survey Data Sets and Its Policy Implications. Ph.D., Economics, 2002. 122 pp. Adviser: Kenneth A. Small
The deregulation experience in airline, banking, and telecommunication suggests that the heterogeneity in consumers' preferences has important policy significance. However, the varied nature in motorists' preferences has been hardly recognized in urban passenger transportation sector. In this public sector, the public authority generally offers a uniform class of services to all potential users. This dissertation employs the new advances in econometrics on survey data sets from road pricing experiment in Los Angeles area to study the diversity in motorists' preferences for travel time and travel time reliability. The empirical findings are used to explore the efficiency and distributional effects of road pricing that accounts for users' heterogeneity. This dissertation found substantial heterogeneity in motorists' preferences for both travel time and travel time reliability. Furthermore, based on a simulation model, this dissertation found that road pricing policies catering to varying preferences can substantially increase efficiency while maintaining the same political feasibility as the current experiments. This dissertation also explores how to apply the recent developments in Bayesian econometrics to estimate the multinomial probit models combining different sources of data, which can be used to estimate the diversity in peoples' preferences with more flexibility in model specification.
The value of travel time savings (VOT) has been an important theme in transportation research because travel time savings is the single largest contributor to the benefits of many transportation projects. It also plays a central role in the cost benefit analysis of the size and scope of public investment. It can shed important light as to whether congestion pricing schemes can increase social welfare. The disaggregate models which are used to derive VOT help us gain insight as to how commuters make their travel decisions. The San Diego I-15 Congestion Pricing Project allows the use of High Occupancy Vehicle lanes by solo drivers for a toll. The toll adjusts every six minutes to maintain free flowing traffic on the High Occupancy/Toll (HOT) lane. Carpoolers get to use the lane for free. This presents us with a unique opportunity to study commuters' choice of a tolled and uncongested alternative versus a free and congested alternative. This thesis studies this decision process based on not only what the commuters say they would do but also on what they actually did. The general result is that the HOT lane is used more by high income, middle aged, homeowners and female commuters. Increased travel time savings and reduced uncertainty in travel time encourages the use of HOT lane. Commuters are more sensitive to variations in travel time in the morning peak than in the afternoon. The toll acts both as a cost of travel and signal of congestion. If the actual toll rises above what the commuter expects then she is more likely to take the lane. The effect of toll also depends on the level of uncertainty in travel times. VOT estimates from Stated Preference data (based on hypothetical responses) are significantly lower than those based on Revealed Preference data (from observed behavior on SDCPP). The difference is consistent and persistent across the different models and methodologies pursued in this thesis. This leads to the conclusion that these differences are real and reflects the difference in responses of individuals to actual and hypothetical situations.
Greenwald, Michael Joseph.The Road Less Traveled: Land Use and Non-Work Travel Relationships in Portland, Oregon.Ph.D., Urban & Regional Planning, 2001. 231 pp. Advisers: Marlon Boarnet and Michael G. McNally.
New Urbanism seeks to exploit a relationship between urban form and travel behavior in order to develop communities which are simultaneously more egalitarian, more pleasant, and less costly to society as a whole. The focus of New Urbanist design practices is to create environments (both urban and suburban) which promote walking and transit over private automobile use as a mode of travel. Specifically, New Urbanists contend higher residential density, closer residential proximity to employment and shopping, grid street patterns and greater access to transit will lead to reductions in automobile travel. This dissertation tests those assertions and discusses the resulting policy implications. The work presented here concentrates on transportation mode choice for non-work travel, defined here as all travel not related to employment or employment related activities. Non-work travel is of particular interest because it comprises a majority of activities involving travel, yet modeling strategies for various policy goals (e.g., clean air, traffic congestion, transit development) ignore non-work travel, in favor of analyzing employment related commute behavior. The working hypothesis is that land use patterns consistent with New Urbanist principles can alter a person's willingness to substitute other travel modes (i.e., walking and/or transit) for automobile use by way of changing the amount of time needed to complete trips by these other modes. This willingness to substitute then impacts the number of trips by each mode of travel observed for individuals. The results described here suggest New Urbanist land use practices can work as their proponents suggest, even when one accounts for the interference of people self-selecting into residential environments which promote one form of travel over others. These findings are tempered by further analysis suggesting New Urbanist designs must have their various elements properly balanced, or none of the proposed benefits will come to pass. Also, it appears that in the context of analyzing distances traveled and number of trips made, New Urbanist practices simply provide a premium on travel that can be completed close to the home. The impact of these findings on theories and policies tied to travel behavior are discussed in the concluding section.
The process of activity scheduling is crucial to the understanding of travel behavior changes. In-depth research is urgently needed to unearth this process. To reveal this process, a new computer program, REACT!, has been developed to collect household activity scheduling data. The program is implemented as a stand-alone program with Internet connectivity for remote data transmission. It also contains a GIS for location identification and a special feature that traces the decisions in scheduling process. A pilot study was conducted in Irvine, California to evaluate the program performance. Experience from the pilot study validated the program's capability of guiding participants to complete data entry tasks on their own, thus the objective of reducing the cost and human resource of such a computerized survey is achieved. Other positive results regarding objectives of reducing instrumental biases and expanding program capabilities were also obtained. Areas for improvement were also identified. Based on the pilot data, activities with shorter duration were found more likely to be opportunistically filled in a schedule already anchored by their longer duration counterparts. In addition, the situations (e.g., location, involved person, and day of the week) under which an activity occurred were found related to its scheduling horizon. Analyses were also performed to validate that the above findings hold in the presence of a third factor (i.e., in-home vs. out-of-home, and work/school vs. non-work/school). Additionally, analysis of tour structure reveals that a certain portion of trip-chains was formed opportunistically. The proportion of opportunistic stops tends to increase as stop sequence increase. Travel time required to reach an activity is also positively related to scheduling horizon of the activity, with distant stop being planned earlier.
The last several years has witnessed a sharp increase in interest in stochastic and dynamic routing and scheduling. Because many systems contain inherently stochastic factors, decisions must often be made before all necessary information is available. To a certain degree, algorithm development has lagged behind implementation. In order to fully leverage advances in information technologies, algorithms which explicitly consider dynamic and stochastic factors should be examined. Or, if static algorithms are to be applied in these dynamic environments, proper attention should be given to examining the conditions under which these perform well. This is the primary theme of this research. This dissertation examines several key dynamic and stochastic routing and scheduling problems: the probabilistic traveling salesman problem, the dynamic traveling salesman problem and the dynamic traveling repair problem. In addition, as part of our research on the dynamic traveling salesman problem, we examine a related M/G/1 queueing problem with switching costs. These problems arise in pickup and delivery operations, repair fleet operations, and emergency vehicle and police operations in addition to many computing, telecommunications and manufacturing applications. As part of our research, we demonstrate that heuristics which rely on partitioning the service region into smaller regions can be very effective for dynamic routing problems. Using a partitioning scheme we show that if a constant guarantee algorithm exists for the k-capacitated median problem, then a constant guarantee algorithm exists for the probabilistic traveling salesman problem. For the DTRP, we show that a partitioning algorithm is asymptotically optimal when the traffic intensity is high. We show that robust a priori algorithms can be developed for dynamic routing problems. For the M/G/1 with switchover cost, we show that an a priori cyclic polling algorithm works very well using both theoretical and simulation analysis. Cyclic polling algorithm also works well for dynamic traveling salesman problem. For these both problems, we identify certain conditions under which the a priori (cyclic polling) solution is close to optimal. We demonstrate that the existence of the connection between the static and dynamic vehicle routing and scheduling problem that have been observed by earlier researchers.
Use of Advanced Traveler Information Systems (ATIS) is considered a promising way to improve traffic condition by helping travelers to efficiently use existing transportation facilities. Unlike other components of advanced management systems, the effectiveness of traveler information technologies is determined primarily by the traveler's awareness of the information, correct interpretation of the information, evaluation of its usefulness, and implementation of the recommended course of action. The problems to be studied in this research are: what information to provide, when, where, and what for. The research examines a wide variety of information dissemination schemes under technologies such as in-vehicle navigation systems, changeable message signs, GPS-based location systems and wireless or Internet based vehicle communication and routing. This study evaluates various route guidance systems via static and dynamic network optimization and traffic simulation models. Parametric studies are conducted on certain aspects, due to the lack of good models on driver response/compliance to ATIS information. This study formulates mathematical problems for the evaluation of both IVNS and CMS as mixed equilibrium traffic assignment problems and evaluates two different route guidance objectives (User Equilibrium and System Optimum) by employing driver's compliance model with varied level of unguided drivers' perception error and market penetration. This study also formulates dynamic optimal route guidance problems and incorporates route guidance strategies into dynamic traffic simulation model. Performance of route guidance strategies for IVNS and CMS are compared via parametric simulation experiments. Special interest of the research is to investigate marketability and effectiveness of private information suppliers who are capable of monitoring traffic condition from their subscribers. The research addresses many issues involved in ATIS dissemination from standpoints of both theoretical evaluation and practical implementation. The dissertation also develops preliminary insights on networks with multiple information service vendors and the complex dynamics that result from it, which is valuable for future research and deployment of ATIS. The research methodology incorporates non-linear network optimization algorithms, heuristic optimizations as well as large network simulation schemes.
We focus our research on a truckload trucking assignment problem aimed specifically at operations supporting the ground movement of intermodal freight within a compact urban area around intermodal facilities. In order to guarantee service, strict time windows are considered in this research. This assignment model has rich practical and theoretical implications. This assignment problem is investigated in several steps. First, a myopic deterministic version is studied in which travel time, service time and the demands are fixed. A new non-decreasing partitioning scheme to deal with time window constraints for this problem is developed. A feasible option for solving the dynamic assignment problem is to repeatedly apply this deterministic algorithm in a dynamic setting in a rolling horizon framework whenever new information is available. The deterministic algorithm provides a basis for further consideration of stochastic factors including queuing times, handling times and travel times under traffic congestion. Several stochastic models are proposed and discussed. The discussion indicates that direct adoption of stochastic models aimed at other problems involves great difficulty because of the complex nature of this problem. Therefore, approximation models are preferable. Further, by incorporating additional requirements of trailer repositioning, a more general problem of multi-layered resource allocation is defined. Multi resource allocation problems have wide practical implications in air, rail and maritime carrier fleet operations. The discussion of these models highlights a promising opportunity for future research. All the methods and ideas motivated by this specific assignment problem can be easily extended to other routing and scheduling problems. As part of this research, we further investigated some NP-hard problems that generally underlie such applications. A special case of TSP problem, titled "the TSP with separation requirement", is examined and a new formulation is presented. The formulation takes the TSP with precedence constraints and the time dependent TSP as special cases. Additionally, a new general cutting plane method is proposed. It applies, but is not limited to, integer programming problem with binary variables. We believe that this method has some advantages over its counterpart, Gomory's method. However, further effort is needed to test its performance.
The dissertation investigates the distinguishing nature of high-technology firm behavior. The first part builds on the previous literature on the economics of industry agglomeration. The major aim of the essay is to distinguish high and low-tech industry groups in benefiting from concentration and size of economic activity inside a locale. The estimation technique is non-linear least squares which is used to estimate a non-linear productivity equation. The non-linearity is based on the theoretical construction which involves an aggregation from county level to state level data. The results are suggestive of the behavioral differences between these two different industry groups. In the second part, I move one step further to examine the distinguishing nature of high-tech inter-firm contracting; I analyze the effect of space, and in particular distance. I base my analysis on the idea that skill transfer among the contracting firms increases the risks of partner deviation from mutual goals. I claim that proximity between the firms enhances monitoring, which could prevent such hazards. Further, based on anecdotal evidence, I hypothesize that in clusters where firms are located in close proximity and form networks, such partner deviation might further be reduced. In this second part of my dissertation, I use a commercial database which includes information on the partnership activity of Silicon Valley firms. The dataset includes 480 inter-firm partnerships. In order to test the above hypothesis, I form a skill quotient variable which is the proportion of scientists, engineers and mathematicians that are employed to achieve the mutual partnership activities. Next, I form two location variables which aim to distinguish the effect of distance versus being located inside a cluster on the form of partnerships. The results provide robust evidence that increased skills and distance induce firms to integrate. Furthermore, firms within clusters choose to engage in inter-firm contracting instead of integrating even when skills are increased.
This dissertation examines commuters' route and scheduling choices in face of travel time uncertainty. A theoretical model is developed to analyze commuters' joint decisions of route and departure time in a simple origin-destination network with two parallel routes; one route passes through free congested lanes on a freeway, whereas a portion of the other consists of free-flowing lanes with time-varying toll. By accounting for trip distance, the theoretical model is able to examine two different sources of travel time uncertainties: that from the length of commute and type of route. The dissertation has also fit various discrete choice models to measure value of time and reliability. The data come from a mail survey conducted in 1998 about commuters on State Route 91 in Orange County, California; these commuters choose between a free and a variably tolled route similar to the theoretical setup. The distribution of travel times across different weeks is measured using loop detector data for each route at each time of day and for each day of the week. The best-fitting models represent travel time by its median, and unreliability by the difference between the 90th percentile and the median; the values of time and reliability are measured by examining commuters' route choice both alone and combined with other choices, namely time of day, car occupancy, and installation of an electronic transponder. The last part of dissertation describes a simulation model to study the travel time profile before and after freeway expansion. The simulation model applies the earlier-mentioned scheduling choice model together with estimates from the empirical estimations. By considering scheduling and route choice by commuters, the time savings as well as the scheduling benefits from expanded road capacity can be measured. The results suggest that the benefits from letting commuters travel closer to their preferred schedules are comparable to time savings on a freeway with moderate congestion; the scheduling benefit increases faster than time savings when congestion worsens. (Abstract shortened by UMI.)
Mattingly, Stephen Peter.Decision Theory for Performance Evaluation of New Technologies Incorporating Institutional Issues: Application to Traffic Control Implementation. Ph.D., Civil Engineering, 2000. 349 pp. Advisers: R. Jayakrishnan and Michael G. McNally
This dissertation develops a new framework for transportation evaluations. Most evaluation techniques fail to adequately assess all factors involved in transportation projects, with qualitative and institutional issues typically receiving less attention than easily quantifiable technical factors. This dissertation uses quantitative decision-theory techniques to develop a flexible approach that allows an analyst to look at all of the myriad issues involved in the evaluation of transportation projects. The research approach focuses on identifying an overall worth, which provides decision-makers with a quantitative measure to compare different system components. The innovative technique developed here integrates the multiple-attribute value function (MAVF) technique with the analytic hierarchy process (ABP). The overall worth of a project may be a combination of its worth under various operational conditions, with subjective relative weights, depending on the decision-makers. A hierarchy of such combinations are possible where the values for individual attributes themselves can be derived from the decision-makers using MAVF schemes. Certain complications arise in the technique, which require the development of a new scaling approach through the use of a universal scaling proxy. The research utilizes a hierarchical approach throughout the analysis while examining a total of four weighting schemes. The methodology is applied to the Anaheim Field Operational Test, a federally funded project, that implemented new traffic control technologies in Anaheim, California's special events area. The research's primary focus is on the city Traffic Engineer's values and preferences over the entire hierarchy. The development of six testing scenarios creates an opportunity to investigate the effects of many evaluation components as well as individual branches within the hierarchy. The evaluation looks at the percentage change in value between the system "before" and "after" implementation across scenarios. While the new system appears to decrease in value for most scenarios, one scenario, the alternate data set, actually shows an overall increase in value. The special event only operations scenario shows improvement over the base case, which indicates the system performs better under these conditions. The evaluation provides valuable insight into the behavior of the system under various conditions and provides guidance for future applications of this evaluation tool.
Moore, Adrian Thomas. The Law and Economics of Privatization: Rent Seeking and Discovery in Privatization Decisions and Processes. Ph.D., Economics, 2000. 141 pp. Advisers: Kenneth A. Small and Daniel Klein
Privatization is often linked with innovation - new ideas the private sector brings to service delivery to cut costs and/or improve quality. But most discussions do not delve into what innovation means in the context of privatization nor into how important it is. Is innovation in privatization merely the replacement of staid government practices with more dynamic private practices? Or is there actual discovery of new practices previously not thought of, or at least not put into practice? I extend the Hayekian/Kirznerian theory of entrepreneurial discovery and develops a theory of discovery in the privatization process. A detailed discussion of privatization of prisons and fire protection services in the United States, reveals that privatization does provide scope and motivation for discovery. These insights are used to show that policy makers, in the course of creating law and policy regarding privatization, should consider the discovery benefits of privatization in their deliberations.
Compin, Nicholas S. The Four Dimensions of Rail Transit Performance: How Administration, Finance, Demographics, and Politics Affect Outcomes. Ph.D., Urban & Regional Planning, 1999. 125 pp. Adviser: Marlon G. Boarnet
The rebirth of rail transit in the US over the past two decades has resulted in rail transit's re-emergence as an integral part of both the physical and economic landscapes of many US cities. Currently fifty-four separate rail transit systems are operated in the US (see Appendix A). This re-emergence of rail transit in cities across the US raises an important question. How does society determine if its investment in rail transit is having an impact? More importantly for the current research: how is the impact of rail transit measured across different geographic regions and system types? Performance standards are one way of determining if public investments are reaching established goals. In this research the impact of variables representing four dimensions of transportation performance: administrative, financial, demographic, and political is assessed. Multiple regression analysis is used to assess the impact of important factors representing each of the four dimensions on the performance of all heavy and light rail transit systems in the US. This study addresses two important gaps in existing research. First, this study is strictly concerned with the performance of rail transit systems; an area of research which is unique and, due to the dearth of information in the past, absent from current literature. Second, existing research has not adequately addressed the impact of specific sources and types of government subsidies on transit system performance. Sources of subsidies include federal, state, and local funding, while types include dedicated and general revenue funding. Results indicate that a significant difference exists between the operation of heavy and light rail transit systems in the US. The main difference is that administrators of heavy rail systems seem to strive to achieve goals more closely associated with standard performance measures, while administrators of light rail systems may target different goals that are not directly associated with or reflected by existing performance measures. The results of this research are extremely useful, not only in terms of determining the impact of important variables on the performance of rail transit systems, but also in helping to focus and redirect performance research.
Logi, Filippo. CARTESIUS: A Cooperative Approach to Real-Time Decision Support for Multi-Jurisdictional Traffic Congestion Management. Ph.D., Civil Engineering , 1999. 197 pp. Adviser: Stephen G. Ritchie
This research describes an innovative distributed approach for the provision of real-time decision support to Transportation Management Center (TMC) operators for coordinated, multi-jurisdictional traffic congestion management on freeway and arterial networks. Coordinated responses among the agencies that share responsibilities for urban traffic management avoids the implementation of operations that may be conflicting or counter-productive. A distributed software architecture, called CARTESIUS (Coordinated Adaptive Real-Time Expert System for Incident management on Urban Systems) was designed, developed and evaluated. CARTESIUS is composed of two interacting, real-time decision-support systems for TMC operator that are able to perform cooperative reasoning and resolve conflicts, for the analysis of non-recurring congestion and the formulation of suitable integrated control responses. The two agents support incident management operations for, respectively, a freeway and an adjacent arterial subnetwork. Each module interacts with a human operator in one of the agencies, is able to receive real-time traffic and control data, and provides the operator with control recommendations in response to the occurrence of incidents. The multi-decision making approach adopted by CARTESIUS reflects the spatial and administrative organization of traffic management agencies, providing a coordinated solution that attempts to satisfy all parties, preserves their own levels of authority, and reflects the inherent distribution of the decision-making power. The structure of the distributed processing and the interaction between the agents is based on the Functionally Accurate, Cooperative (FA/C) paradigm, a distributed problem solving approach aimed at producing consistent global solutions even when complete and up-to-date information is not directly available to the agents, in order to reduce communication requirements and synchronization time delays. The contribution of this research lies in demonstrating the validity of the assumption that satisfying control solutions can be efficiently obtained by relaxing the requirement that agents have shared access to all globally available information, and the application of theoretical principles of the FA/C paradigm to traffic control, through the development of CARTESIUS. The simulation-based validation of the system performance has demonstrated the effectiveness of such an approach in producing real-time, integrated traffic control solutions that reduce the adverse impact of incidents on traffic circulation, network-wide.
This dissertation presents several travel behavior models related to the 91 Express Lanes. The 91 Express Lanes are a facility in Orange County, California that opened in December 1995. The Express Lanes are built in the median of an existing freeway and offer users a congestion-free tolled alternative to the heavy traffic on the regular, general purpose lanes. The facility requires an electronic transponder and has tolls that change by time of day so that traffic flows freely. Many transponder-owners use the Express Lanes only infrequently. Although the Express Lanes were built and are operated by a private company, for the first two years, carpools with three or more people could travel on the Lanes without paying the toll making the facility a High Occupancy/Toll (HOT) lane. The models in the dissertation look at the main objective: what accounts for use of the facility. In addition to presenting summary statistics from the mail-based survey conducted by researchers at University of California, Irvine, several models are proposed, estimated, and analyzed. One chapter presents models of use and frequency of use. The "hurdle" of obtaining the electronic transponder is also considered. One chapter considers revealed preference and stated preference (RP and SP) models of the real-time decision to use the Express Lanes by infrequent Express Lane users. Another chapter looks at RP and SP data models of carpooling in the corridor. Income plays a role in all of the models in intuitive ways. Yet, I can argue that income does not have an overwhelming effect on all of the behavioral decisions related to the toll road. Cultural differences and education influence having or not having a transponder. The independent variables tested explained little in the models of real-time choice which suggest that the decision to use the lanes may not be occurring due to real traffic conditions, but to each individual's extenuating circumstances (for example, having to get to a meeting). There is little here to support the hypothesis that HOT lanes encourage carpooling, but the evidence shows that carpooling remains stable in the corridor.
Reja, Binyam. Essays
in the Political Economy of Contracting: An Institutional Analysis of
Private Sector Participation in Urban Public Transport. Ph.D., Economics, 1999. 130 pp. Adviser: Linda R. Cohen
This dissertation contains three essays. The first essay deals with the political economy of contracting out with the private sector in the US transit industry. A model of bus contracting is developed to assess whether contracting out, as an alternative institutional arrangement, is feasible given the political economy of the US transit industry. The essay finds that significant constraints exist that hinder a more extensive use of contracting out in the US transit industry. The second essay deals with public transport organization in developing countries. It develops a model of public transport organization by examining the cost structures of the scheduled service and the informal transport providers. The implication of the model for bus franchising is developed and tested using Jamaica's experience with bus franchising. Finally, the third essay uses the same theoretical framework employed in the first and second essays to develop a model of the organization of corruption in developing countries, and to construct a hypothesis on how corruption is different in Asia than in Africa.
Forecasting the demand for alternative-fuel vehicles (AFVs) is quite important for manufacturers, fuel suppliers and environmental planners. AFVs have attributes such as reduced range and limited refueling options that are very different from existing vehicles. Therefore stated preference (SP) data is necessary for demand models. Previous work by Brownstone, Bunch, and Train (1998) shows that there are serious biases in these stated preference data. Another source of households' vehicle preference, is households' actual observed transaction behavior (Revealed preference (RP) data). I develop a dynamic stated and revealed preference vehicle transaction model which uses the RP data to control for the biases of using pure SP data in order to better forecast households' demand for AFVs for California. I implement a "scale factor" to specify the relationship of the different variances of the RP and SP data. Moreover, I examine the nested structure over different fuel-type vehicle choices and estimate both the multinomial logit (MNL) and nested logit (NL) models. In addition, I conduct forecast using the pure SP and joint SP-RP MNL models under the scenario consisting of new vehicle technologies for year 1998. Compared to the new vehicle sales statistics, it is obvious that the joint SP-RP model provides more reasonable forecasts. I also examine the different substitution patterns implied by the pure SP MNL and NL models when new vehicle choices are introduced. The NL model predicts more realistic substitution pattern. I also add the used vehicle choices to the forecast scenario to make the forecast more realistic because the used vehicle market is taken into consideration. Large panel data sets have been collected by the California Alternative-Fuel Vehicle Demand Forecast Project since May 1993. These data contain extensive information on households' stated and revealed preference vehicle transactions, vehicle utilization and households' socioeconomic characteristics. This study serves as an example of how to forecast new products or technologies that mark considerable departures from existing products or technologies.
Sun, Carlos Chung I. Use of Vehicle Signature Analysis and Lexicographic Optimization for Vehicle Re-identification on Freeways. 170 pp. Ph.D., Civil Engineering, 1998. Advisers: Stephen G. Ritchie and R. Jayakrishnan.
This dissertation presents the vehicle reidentification problem formulated as a lexicographic optimization problem. The lexicographic optimization formulation is a preemptive multi-objective formulation that combines goal programming, classification, and Bayesian analysis techniques. The details of field implementation and data collection design are also presented. The solution of the vehicle reidentification problem has the potential to yield reliable section measures such as travel times and densities, and enables the measurement of specific dynamic origin/destination demands as well as the development of new algorithms for ATMIS (Advanced Transportation Management and Information Systems) implementations of the approach using conventional surveillance infrastructure. Freeway inductive loop data from SR-24 in Lafayette, California, demonstrates that robust results can be obtained under different traffic flow conditions. A discussion is also presented of the application of section densities in a dynamic origin/destination demand estimation framework as an example of the usefulness of this approach. The use of existing surveillance infrastructure coupled with this approach could allow development of widespread applications in Intelligent Transportation Systems (ITS).
A typical urban traffic network is a very complicated large-scale stochastic system which consists of many interconnected signalized traffic intersections. Setting signals at intersections so that the traffic in such a network flows efficiently is a key goal in traffic management. The conventional traffic signal control algorithms assume the traffic system is deterministic; most of them use data aggregation, instead of a mathematical model, and apply off-line, heuristic control strategies which do not respond to the fluctuations of the traffic flows in the network. In this dissertation, the traffic signal control problem is formulated as a decision-making problem for a stochastic dynamical system. Based on Markovian decision theory, a new decentralized optimal control strategy with the embedded platoon dispersion model is developed to minimize the queue length and the steady state delay of traffic networks. A rolling horizon algorithm is also employed to achieve real-time adaptive traffic signal control. Statistical analysis of the computer simulation results for this approach indicates significant improvement over the traditional fully actuated control, especially under the conditions of high, but not saturated, traffic demand.
Chen, Anthony. Formulation of the Dynamic Traffic Assignment Problem with an Analytically Embedded Traffic Model. Ph.D., Civil Engineering, 1997. 230 pp. Adviser: R. Jayakrishnan
Dynamic Traffic Assignment (DTA) has been identified as the backbone of the two major systems, Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS), in the general Intelligent Transportation Systems (ITS) framework developed to make traveling quicker, easier, safer, and cleaner. The success of ATMS/ATIS depends greatly on DTA to provide timely and accurate estimates of current and future states of the traffic system. In this dissertation, a radically new approach is developed to solve the DTA problem. This approach analytically embeds a hydrodynamic flow model as a simulation into an optimization-based DTA framework. This is the first analytical DTA model where simulation equations are incorporated as constraints to move traffic that respect the first-in-first-out (FIFO) requirement in an optimization formulation for network assignment. It obviates the difficulty of imposing external FIFO constraints that may not be consistent or justified with observed or theoretical traffic flow behavior. A distinct feature of this approach is the use of traffic load (number of vehicles) as the assignment variable which results in convex travel time functions that are realistic for both uncongested and congested traffic conditions. Another unique feature of the framework is the use of small time-discretizations, with the theoretical correctness of the model improving with decreasing time step lengths. Network assignment is accomplished for the time-dependent origin-destination demands based on a two-level optimization framework which fixes the incidence indicator between the time-dependent paths and their constituent links at one level and then assigns traffic similar to a static traffic assignment at the other level. Using this analytically embedded framework, four DTA models are developed in this dissertation to address different requirements in ATMS/ATIS. Specifically, it solves both user equilibrium and system optimal traffic assignments in the same analytical DTA framework. Traffic dynamics on freeways integrated together with arterial streets for both uncongested and congested traffic conditions are captured by a queuing version of the analytically embedded DTA model. The described solution algorithms look for off-line evaluation, but on-line applications such as short-term traffic prediction and route guidance have greater needs. Several dynamic rolling horizon DTA models are formulated according to the capability to re-route previously assigned vehicles. Solution procedures have been designed, implemented, and applied to various networks, including the Anaheim Testbed network, with considerable success.
This dissertation examines the relationship between transportation access and industrial and office property rents. The primary purpose of this research is to evaluate two sparsely studied topics in the transportation-land use literature: the impacts of light rail transit on property values, and the effect of transportation facilities on non-residential land uses. Multivariate regression analysis is used on longitudinal data for approximately five hundred and twenty office properties and five hundred industrial properties collected from the San Diego metropolitan region over the period from 1986 to 1995. Asking rents ($/square foot/month) is the dependent variable. Straight-line distance of each property to the nearest freeway on/off ramp, the nearest light rail station, and to the San Diego central business district provide measures of access. Other independent variables include building and neighborhood characteristics. The findings show that access to freeways is consistently significant in predicting office rents. This result indicates that freeways are important in shaping office property values, and by extension office land use patterns. Light rail transit did not have a significant effect on office rents. Access to the CBD was only significant for downtown office properties. The CBD variable in this case may be a proxy for the effect of localization economies. None of the measures of access was significant for industrial properties. This research underscores the importance of refining measures of access in order to capture and better understand the transportation-land use relationship. In particular, if the distance of an industrial firm to freeways, light rail transit, and the CBD is not important, then what kinds of access do matter? This research also has important implications for planning light rail transit systems. There is strong evidence that light rail systems do not provide enough travel cost savings to increase non-residential property values. This finding should be taken seriously in planning alignments for future light rail systems. Light rail systems need to be aligned with existing activity centers, rather than expected to stimulate new development or the redevelopment of distressed urban areas.
Sandeen, Beverly Ann. Transportation
Experiences of Older Suburban Adults: Implications of the Loss of the
Driver's License for Psychological Well-Being, Health and Mobility. Ph.D., Social Ecology, 1997. 189 pp. Advisers: Karen S. Rook and Daniel Stokols
The number of elderly adults in the United States is growing, and, by the year 2030, it is estimated that 21 percent of the population will be aged 65 and over. Along with the transformation in age structure, the United States has also become suburbanized. Suburbs generally offer few transportation alternatives to the private automobile, and, if older adults age in place, they may face difficulty accessing resources when they stop driving. This study utilized three theoretical perspectives--transitional processes, person-environment fit, and stress and coping--to guide the development of a model for examining how loss of the driver's license negatively affects psychological well-being, health, and mobility. Sixty-four drivers and sixteen former drivers were interviewed by telephone or in person. Interviews assessed transportation history, well-being, coping strategies, health background, and demographic information. Participants also were asked to draw cognitive maps of their weekly travels, and they completed two questionnaires concerning life stress and driving self-efficacy. Drivers were placed into two groups based on driving patterns and behaviors: modified drivers, who had made substantial changes in their driving patterns (e.g., not driving at night), and regular drivers, who had not made changes in their driving patterns. Results indicate that former drivers have significantly lower levels of well-being than do regular drivers, controlling for age, education level, and number of ailments. Supportive housing was associated with higher levels of life satisfaction for modified and regular drivers but lower life satisfaction for former drivers. Former drivers who had no prior transit experience had much lower life satisfaction than did any other group. While these findings are correlational in nature, they suggest that loss of the license may affect well-being and that some environmental and personal resources may moderate this relationship. Additional research should be conducted to inform policymakers and planners about how older adults living in suburbs may be constrained and adversely affected by the loss of access to the private automobile. Meeting the needs of older adults through transportation and telecommunication technology should also be examined.
Lane-changing is an important issue in modeling traffic movements since it influences intra-lane and inter-lane traffic characteristics. Mandatory lane changing caused by incidents can, more importantly, be treated as a distinct pattern of traffic for use in automatic incident detection and characterization. This research describes a new method for real-time prediction of vehicular lane-changing probabilities and queue lengths during incidents. The proposed method consists of a discrete-time nonlinear stochastic system for modeling vehicular lane-changing behavior during incidents, and a recursive estimation algorithm for estimating state variables in the stochastic system. The noise terms of the recursive equations in the model account for the influence of queues and the variability of traffic arrival patterns on incident lane-changing maneuvers. The effect of traffic control signal on state variable prediction is involved in formulating the recursive equations in the case of intersection incidents. The techniques utilized in developing the recursive estimation algorithm include the use of an extended Kalman filter, truncation, normalization, and the updating queue lengths. Lane traffic counts are the sole input data used in this method. These data are readily collected from point detectors based on the proposed detector configurations. In addition, two extended methods for application to real-time incident detection using MSPRT (Modified Sequential Probability Ratio Tests) and queue-overflow prediction are developed on the basis of the proposed stochastic modeling approach. The data sources used in model tests include simulation data generated either from TRAF-NETSIM 5.0 for surface streets or INTRAS for freeways, and real data. Test results have shown the feasibility of predicting real-time lane-changing probabilities employing the proposed approach, and the applicability of the extended methods to real-time incident detection and queue-overflow prediction. The research presented here may also help stimulate research in related areas such as incident management systems, automatic vehicle tracking and monitoring systems, and automatic road congestion warning systems for further use in ATMS and ATIS.
Subbaraman, Chittur. Network
Surveillance Supported Object-Based and Task-Based Time-Bounded Fault
Tolerance Schemes and their Incorporation into a Timeliness-Guaranteed
Kernel. Ph.D., Electrical & Computer Engineering, 1997. 218 pp. Adviser: Kwang H. (Kane) Kim
Real-time fault tolerance (RTFT) is a core technology for increasing the reliability of computer-based safety-critical applications such as space applications, factory automation systems, etc. In recent years, the real-time computing market has started showing explosive growth. In order to realize highly robust real-time fault tolerant computing stations, several component techniques are necessary. Among the most significant include (a) a scaleable RTFT scheme, (b) a network surveillance (NS) scheme, (c) a timeliness-guaranteed kernel that supports both the RTFT and the NS schemes. This dissertation attempts to make a significant step forward towards the goal of realizing ultra-reliable computer-based safety-critical systems. As a first step in this direction, the following new technologies have been devised: (i) the primary-shadow time-triggered message-triggered object (TMO) replication (PSTR) scheme which provides time-bounded recovery from faults in TMO structured systems, (ii) the supervisor-based network surveillance (SNS) scheme which is effective in a variety of point-to-point networks and is amenable to fault detection latency bound analysis. Second, it was observed that even though a few promising component technologies that addressed certain specific requirements of real-time fault tolerant computing stations have been established, little efforts were made to integrate these technologies. Only such integrated technologies can meet the diverse demands that are imposed by safety-critical applications. This dissertation attempts to establish guidelines for such integration. The following integrated schemes have been devised: (i) the PSTR scheme and the SNS scheme, (ii) the distributed recovery block (DRB) scheme established earlier and the SNS scheme, (iii) the adaptable DRB scheme established earlier and the SNS scheme. Third, convincing demonstrations of the validity and potential utility of the devised schemes would facilitate their use in real-world applications. A timeliness-guaranteed kernel developed earlier was extended to support all the devised schemes. A TMO-structured defense application supported by the newly extended kernel was also made fault-tolerant. Finally, the performance analyses of the RTFT and NS schemes, even though of great importance, have been scarcely practiced. We have analyzed the performance of the devised schemes and obtained some tight time bounds. The modeling and analysis techniques presented would serve as useful guides to system engineers.
This dissertation documents the development of optimization models in a mixed integer-linear form for the control of network traffic signalized intersections. The existing network traffic signal optimization formulations usually do not include traffic flow models, except for control schemes such as SCOOT that use simulation for heuristic optimization. Other conventional models normally use isolated intersection optimization with traffic arrival prediction using detector information, or optimization schemes based on green bandwidth. In this dissertation a complete formulation of the problem that includes explicit constraints to model the movement of traffic along the streets between the intersections in a time-expanded network is presented, as well as constraints to capture the permitted movements from modern signal controllers. The platoon dispersion model used is the well-known Robertson's model, which forms linear constraints. Thus it is a rare example of a traffic simulation being analytically embedded in an optimization formulation. The formulation is an integer-linear program, and does not assume fixed cycle lengths or phase sequences. It assumes full information on external inputs, but can be incorporated in a sensor-based environment. The integer-linear program formulation may not be efficiently solved with standard simplex and branch and bound techniques. We discuss network programming formulations to handle the linear platoon dispersion equations and the integer constraints at the intersections. A special purpose network simplex algorithm for fast solution is addressed in the proposed solution approach. The optimization model takes the form of mixed integer linear programming. The control strategies generated by these optimization models were compared with those derived from conventional signal timing models, using the TRAF-NETSIM microscopic simulation model. It was found that the optimization models successfully produced optimal signal timing plans for the various signalized intersections including simulated and real-world networks. The proposed optimization models consistently outperformed the conventional signal control methods with respect to system delay objective. This conclusion was drawn from the TRAF-NETSIM simulation.
A universal freeway incident detection framework is a task that remains unfulfilled despite the promising approaches that have been recently explored. Only recently, researchers and practitioners have begun to increasingly realize that for an incident detection framework to be universally operational and successful, it needs to fulfill all components of a set of recognized needs. It is the objective of this research to define those universality requirements and produce an incident detection framework that possesses the potential to fulfill them. A new potentially universal freeway incident detection framework has been proposed, developed and evaluated. The research effort was started by defining a comprehensive set of requirements that any universal incident detection algorithm or framework should fulfill. Among these requirements, an incident detection needs to be operationally accurate, automatically transferable, and capable of automatically adapting to changes in the freeway environment. This set of universality requirements was used as a template against which all algorithms within the scope of this study have been evaluated. The universality of the most well known existing incident detection algorithms was tested. Serious lack of universality, particularly transferability, was detected in all existing algorithms. Preliminary investigation of two promising advanced neural networks, namely the LOGICON and the PNN, was conducted. The PNN was more appealing due to its universality potential. The PNN was modified using a principal components transformation layer that resulted in performance enhancements, together with its potential universality lead to the choice of the modified PNN for in-depth development. The in-depth development stage was divided into three phases: feature extraction, on-site real time retraining of the PNN after transferability, and development of a post processor output interpreter. The overall PNN-based framework was found to be fully complaint with the entire set of universality requirements. Finally a new approach for training a multi smoothing parameters version of the PNN was investigated. The approach utilized genetic algorithms for optimizing the selection of the smoothing parameters. Obtained results indicated an improvement in performance over the single smoothing parameter PNN but on the expense of longer training time.
In an effort to compensate for the deficiencies on traditional trip based approach, this dissertation focuses on the supplement in traditional measures of individual accessibility, and the incorporation of temporal transference effects and ride sharing behavior within a household to form a sensitive index. A network-based activity assignment protocol has been developed for complex travel activity decisions within a household. The proposed research incorporates routing, scheduling, and ride-sharing components into a hybrid model that explicitly captures the interactions between household members and integrates ride-sharing, and time window constraints. Under this approach, individual accessibility can be estimated and aggregated to reflect household accessibility. Prior research on such accessibility approaches strongly suggests that the proposed extensions can be employed to estimate the impacts of changes in different policy options. Results of this research contribute to the state-of-the-art in complex travel behavior and validate a policy-sensitive forecasting model.
Crane, Soheila Soltani. An Empirical Study of Alternative Fuel Vehicle Choice by Commercial Fleets: Lessons in Transportation Choices, Cost Efficiency, and Public Ag.encies' Organization. Ph.D., Economics, 1996. 135 pp. Advisers: David Brownstone and Linda Cohen
The concern about air pollution has led government agencies to design and implement mandates to replace some commercial fleets' gasoline vehicles with Alternative Fuel Vehicles (AFVs). In Part One of this dissertation, I investigate the diffusion of AFVs in the commercial sector. Commercial fleets are frequently the first target of government regulation because policy agencies can target a large number of vehicles while regulating fewer establishments relative to the household sector. Using stated preference survey data from over 2000 commercial and local government fleets in California, I estimate multinomial logit and nested logit models of fuel choice that predict the probability of choosing each type of AFV. Given certain assumptions about vehicle technology, these models predict that starting in year 2010, almost 17% of new vehicle purchases by the commercial and local government fleets will be electric, about 20% will be compressed natural gas, and almost 21% will be methanol vehicles. I find that fuel choice probabilities differ depending on the market structure. Public agencies seem to be more AFV friendly than private firms. Important factors in fleet vehicle choice are the degree of familiarity of the firm' s staff with the AFV operation, the size of the establishment, government regulations, and the availability of the refueling infrastructure. In Part Two, I review hypotheses about the determinants of local government agencies' efficiency and use the stated preference survey data to test these hypotheses. Public choice models predict systematic differences among government agencies regarding their cost considerations and sensitivity to environmental issues. The empirical evidence identifies two factors that affect government agencies' performance. The first factor is jurisdiction: an agency that has a more rigid boundary, such as a city or a county, seems to operate more efficiently than an agency that has more flexible geographic boundaries, as is the case with the special districts. The second factor is direct citizen voting: an agency director who is subject to re-election seems to coordinate a more efficient agency operation than one that is appointed to the job as a career position.
Unreliable travel time is defined to mean a distribution of possible commute durations. This dissertation identifies occupational groups and shows how an individual's occupation can be expected to indicate how that person is going to behave in risky commuting situations. Individual occupations attract a certain personality type. Also, individual occupations require different amounts of team work and pose idiosyncratic supervisory requirements for the employer. These effects create systematic variations among employer imposed work rules concerning employee's time use and employee expectations and reactions to the rules. The outcome is both personality driven and situation specific response to risky commuting situations. A psychological construct--locus of control--draws a boundary between what an individual believes is influenced by her own actions and what is caused by factors external to her. A person with an internal locus of control is optimistic about her possibilities to influence the outcomes of risky situations, while a person with an external locus of control tends to see the cause of events as random or influenced by some powerful others. Commuters with an external locus of control take fewer planned risks, reserving more slack time between planned arrival and official work start time. If something unanticipated throws them off the habitual path, they are less likely to go out of their way to maintain the planned arrival time. The commuters with more internal locus of control are more willing to take planned risks and are more committed to see that the risk pays off. I use occupational classification developed by John Holland and resource exchange theory of Uriel Foa to establish a partial order from most external to most internal occupational groups. The dissertation also includes models where the commuter trades off different elements of unreliable travel time: expected mean travel time, expected schedule delay early, and expected schedule delay late. Occupations affect these tradeoffs even when income and family composition are controlled.
Urban bus transit is an example of a public industry which relies on subsidies for survival. The history of mass transit in the United States reveals that the impetus for government subsidies can partially be attributed to factors exogenous to the industry. Subsidies have created incentives for distorted or sub-optimal input choices among the firms. Previous productivity studies of urban bus transit firms have not properly accounted for the effects of these incentives and the ambiguous nature of optimization decisions inherent in such an institutional environment. Furthermore, transit firms operate as spatial monopolies in almost all urban areas. I argue that in this situation, non-parametric frontier estimates of efficiency are appropriate measures for the analysis of productivity. I also investigate and compare several proposed modifications to the non-parametric estimation techniques. When the efficiency estimation technique is configured to the institutional environment facing this industry not only are some important results from previous studies of efficiency in transportation firms confirmed, but I also show that other factors postulated to explain differences in transit cost efficiency do not help explain differences in measures of technical efficiency.
van Hengel, Drusilla Ruth. Citizens Near the Path of Least Resistance: Travel Behavior of Century Freeway Corridor Residents. Ph.D., Urban & Regional Planning, 1996. 226 pp. Advisers: Joseph F. Di Mento and Will Recker
This work joins a body of literature that tests whether commuting data support: (a) a hypothesized mismatch between employment or shopping opportunities among isolated groups of urban residents; and (b) the equitable distribution of mobility benefits following the opening of a major urban freeway. A history of the urban interstate system and the legislation guiding its construction is provided first as a background to the study. Second, a social ecological interpretation of the multi-dimensional effects of a change in urban form is introduced with a specific orientation toward freeway sitings. New highway impacts vary depending upon the condition of the surrounding area and proximity to the facility. Three grouping variables are introduced as possible means through which to categorize residents severely impacted by the construction of the Glenn M. Anderson (Century) Freeway/Transitway (Interstate 105). A behavioral measure segments residents based on the social and economic conditions in their census tracts. Two geographic grouping variables separate inner city residents from more suburban residents and residents close to the right-of-way from those more than a mile from the construction. U.S. Census data illustrate the social and economic differences among these groups within the Century Freeway corridor area. It is determined that, at an aggregate level, mean travel time to work is longer for residents of distressed areas, central city areas and residents near the right of way. Residents in the study area are surveyed at two points in time. Baseline travel behavior analyses indicate that controlling for race, education, income, and mode choice, the work trip of South Central Los Angeles residents is longer than neighboring areas in the corridor. Also, this trip is longer for residents living within one mile of the freeway. The behavioral variable does not aid in the discrimination of work trip travel times. Analysis of transportation behavior subsequent to the freeway opening reveals that the travel time savings for work and nonwork trips are unequally distributed across the study area. Significantly, the freeway opening is not associated with a convergence of work trip travel times. Those least affected by highway construction demonstrate travel benefits that are not found among severely impacted respondents.
The goal of this dissertation is to develop an activity-based trip generation model which addresses shortcomings of the conventional trip-based approach. Problems with conventional generation models resulted from a fundamental incapability to address the temporal and spatial characteristics of activities and the trips which they generated. The sequencing and scheduling of trips and activities, and interactions between household members, are ignored in the standard model. The proposed activity-based generation model was developed to estimate trip production from the analysis of complete travel/activity patterns. This approach classifies travel patterns with respect to activity, spatial, and temporal characteristics; standard trip rates can be also estimated from these representative activity patterns. In addition to a standard category production model, a stochastic logit-based pattern choice model and a deterministic discriminant analysis model were developed to simulate activity pattern choice and the associated trip production level. A variety of variables describing the socioeconomic and demographic attributes at the household or person level comprise the utility functions for choice prediction. Temporal stability of activity patterns was evident in similar life cycle groups in the 1985 and 1994 Portland test data, supporting the conclusion that patterns are a viable structure on which to base future forecasts.
Of the factors that have helped shape metropolitan areas, transportation has probably had the greatest impact. This dissertation focusses on three issues related to travel, mobility and urban form in the United States. Chapter 1 analyzes the determinants of households without vehicles to determine whether the choice to not have a vehicle is related to transportation issues or socioeconomic issues. The finding points toward socioeconomic characteristics, yet the findings are inconclusive on transportation and access matters due to constraints of available data. Chapter 2 addresses land use characteristics at the destination of the commute. Different definitions of land use yield slightly different and significant results as it relates to planning policy. Chapter Three addresses neighborhood street configurations and non-work travel. It is found that some notions of neotraditional development i.e., the grid network versus the cul-de-sac are irrelevant when controlling for other travel and socioeconomic factors. This contributes to a debate that has benefitted from little empirical research.
Despite recent improvements, Southern California experiences some of the worst air pollution nationwide. California has passed the strictest emission regulation in the nation to deal with the problem. The most controversial regulation mandates the sale of zero-emission vehicles: 2% of automobile sales by the major manufacturers must be zero-emission vehicles in 1998, 5% in 2001, and 10% by 2003. But simply mandating sales does not fully address the problem. Questions still remain: Under reasonable technological assumptions, what will the demand for alternative-fuel vehicles be? Will this demand greatly reduce emissions in Southern California? And if so, by how much? My dissertation addresses these important questions through the use of a dynamic microsimulation model. Microsimulation models begin with a sample of households or firms from the population. Each period the sample is faced with changing circumstances (such as the introduction of a new vehicle type), and their response is forecast based on models of their decision-making process. Since automobiles are a large consumer durable that must meet the needs of the entire household, when the household undergoes a demographic change, their vehicle needs will change. It is important to model household changes as part of the simulation process. In the first part of my dissertation, I develop demographic models which are used to simulate household changes. They extend previous models in three main ways: (1) by using continuous time hazard models, (2) by allowing for inter-dependencies across the various types of change that a household may undergo, and (3) by including several important explanatory variables such as race, gender, income, education, employment status, and indicators of previous demographic changes. I then run the microsimulation model under several different assumptions about the availability of alternative-fuel vehicles, vehicle prices, operating characteristics, fuel prices, and fuel availability. For each run, I determine total emissions using the forecasts of vehicles by vintage and fuel type, mileage estimates for each vehicle, and emission factors for each vehicle. I look at scenarios with different purchase price assumptions for electric vehicles, without the option of electric vehicles, and with different purchase price assumptions for CNG vehicles. Based on my comparison of the scenarios, I find that reducing the price of alternative-fuel vehicles does not necessarily lead to reductions in emissions. During the first few years, emission levels may actually increase if households trade off usage between a limited range alternative-fuel vehicle, and their second or third vehicle (which is typically an older gasoline vehicle). I also find that the option of electric vehicles leads to a definite and immediate improvement in emissions (or conversely, that removing the option of electric vehicles increases emissions). Using cost estimates from Small and Kazimi (1995), the health benefits of those emission reductions are valued at between $40 million and /$140 million. While a significant benefit, it is the same the magnitude as the United States Advanced Battery Consortium's yearly research budget. Since the battery consortium's budget is only a tiny fraction of the costs associated with the current electric vehicle mandates, the most prudent policy may be to abandon the current mandates for more cost effective policies.
A major concern in Advanced Transportation Management Systems (ATMS), one of the principal thrusts of the national program on Intelligent Transportation Systems (ITS), is providing decision support to effectively detect, verify and develop response strategies for incidents that disrupt the flow of traffic. A key element of providing such support is automating the process of detecting operational problems on large area networks. Successful detection of operational problems in their early stages is vital for formulating response strategies such as modifying surface street signal timing plans and activating or updating traveler information systems, including changeable message signs, in-vehicle navigation systems and highway advisory radio, altering emergency services, amongst others. Reliable surface street incident detection is also necessary for the development of integrated freeway-arterial control systems. Incident detection has been the subject of research for the past two decades. But the focus has been on detecting capacity reducing non-recurring congestion on freeways. Only recently has attention begun to focus on developing a methodology for surface street networks. The main focus of this research was to develop a methodology to detect different types of operational problems relevant to the operations of surface street networks. In this research, a modular architecture of neural network has been proposed to develop a comprehensive system to detect different types of operational problems, based on detector data from an urban traffic control system. The modularity of the classifier proposed decomposed the task of detecting different types of problems and produced an overall system of models that individually outperformed a single multi-layer feed-forward neural network model for lane-blocking incidents, special event conditions and detector malfunction, and also a statistically-based discriminant function model. The neural network-based models and the statistical models were developed and tested with simulated and field data from two test study areas in Anaheim and Los Angeles, California, USA. The higher detection rates and lower false alarm rates of the modular neural network model compared to other techniques demonstrated its potential of detecting different types of traffic operational problems on urban arterials.
Ren, Weiping.A Vehicle Transactions Choice Model for Use in Forecasting Demand for Alternative-Fuel Vehicles Conditioned on Current Vehicle Holdings. Ph.D., Economics, 1995. 119 pp. Adviser: David Brownstone
California has mandated the introduction and sale of low-emission (compressed natural gas and methanol) and zero-emission (electric) vehicles to displace conventional-fuel vehicles. Other states are considering following California's lead. Hence, forecasting the demand for alternative-fuel vehicles is critical for private automobile manufacturers faced with designing and marketing alternative-fuel vehicles, for utility companies in their demand-side management planning, and for public agencies in their evaluation of incentive schemes. I develop a new conditional logit model where the choice alternatives are vehicle transactions rather than vehicle holdings. This conditional transaction model is closer to the true decision process of purchasing a vehicle than are previous vehicle demand models. This model is designed to be incorporated into a dynamic microsimulation submodel of a model system designed to simulate the dynamics of the new vehicle adoption process and to produce a separate forecast for each period. Since the next period's forecast must depend on all the previous forecasts, it is desirable to focus on vehicle transactions rather than vehicle holdings, and to calibrate dynamic behavioral models that use panel data. The model for the first time uses both vehicle purchase information and vehicle holding information and forecasts demand for stated preference (SP) vehicles conditioned on the revealed preference (RP) vehicle holdings. Forecasting SP vehicle choices by conditioning on RP vehicle holdings can also capture some heterogeneity between households and avoid some possible bias problems relative to vehicle holding models. Based on one scenario for vehicle technology in 1998, a preliminary forecast has been done for one- and two-vehicle households that have stated they would purchase new vehicles. The forecast purchase share percentage (with 90 percent confidence band) for gasoline vehicles is 63.6 (55.4-68.3); for methanol vehicles is 14.3 (11.8-17.3); for compressed natural gas vehicles is 19.3 (16.2-23.6); for electric vehicles is 3.2 (2.5-4.0). The forecast implies that if the scenario is accurate, then manufacturers will be able to sell more than enough alternative-fuel vehicles to meet the current California mandates. Although this model is developed here to forecast the demand for alternative-fuel vehicles and gasoline vehicles, it can also forecast the demand for gasoline vehicles alone. So, I use the model to forecast the transaction behavior and gasoline vehicle demand for one- and two-vehicle households which have transactions during the first and second waves of the survey, which are approximately one year apart.
Understanding travel and residential mobility behavior is crucial for formulating urban policies and planning urban infrastructure. These decisions shape urban structure, and may contribute to problems such as congestion, air pollution, urban decline, and urban sprawl. The first part of the dissertation examines differences in commuting patterns between men and women, as a function of differences in household composition and household division of labor. I find that single men and single women have similar travel patterns, but the travel patterns of men and women with families differ from each other. Gender differences are particularly important in making a side trip, but less so in mode choice and trip scheduling. They arise mainly from the differential effects of household composition on men and women. In particular, having children adds side trips to mothers, but not to fathers. Men are less likely to make a side trip when there is another adult in the household, especially when this adult does not work. But women do not seem to have a similar advantage. Women tend to ride with family when there is another adult in the household. The second part of the dissertation examines residential mobility, advancing the literature by: (1) using hazard models within a competing risks framework to model different types of moves; (2) using the individual as a unit of analysis; (3) accounting for undeserved heterogeneity; and (4) testing for effects of accessibility and neighborhood characteristics. The results establish important differences in the determinants of different types of moves. For example, any change in household income stimulates own-to-own, rent-to-own, and rent-to-rent moves; but only a decrease in income stimulates an own-to-rent move. Changes in household size are unimportant in rent-to-own moves, but they stimulate own-to-own and rent-to-rent moves. Only a decrease in household size stimulates own-to-rent moves. Wealthier households are more likely to move from owner-to-owner and renter-to-owner. Larger households are less likely to make rent-to-rent moves. Generally, renters are more likely to move. Age is important in determining rent-to-own moves: mobility initially increases until age 41, and then decreases. Job changes stimulate own-to-own and own-to-rent moves.
Zhang, Hongjun. A New Framework for Optimal Freeway Ramp Control. Ph.D., Engineering, 1995. 95 pp. Adviser: Stephen G. Ritchie
In an effort to relieve peak hour congestion on freeways, various ramp metering algorithms have been employed to regulate the inputs to freeways from entry ramps. This dissertation provides a framework to examine the effectiveness of ramp metering under recurrent congestion. The framework treats the ramp control problem in a dynamic system's setting that incorporates traffic modeling and dynamic optimization. A system that consists of a freeway section and its entry and exit ramps is identified. The performance of this system under ramp control, chosen as the total time spent in it for all system users, is then evaluated for various traffic conditions during a commuting peak, under the assumptions that traffic follows the rules prescribed by the LWR theory, and the system has to serve all its demand. Based on this analysis, traffic conditions are broken down into a number of categories according to the impacts of ramp metering to system performance. The results obtained in this dissertation may assist in the formulation of ramp metering policy in heavily congested urban areas by providing a clear breakdown of conditions and parameters necessary for successful operation of ramp metering under congested conditions. For traffic conditions where ramp metering is effective to improve system performance, new ramp metering algorithms are also proposed.
A major source of urban freeway delay in the United States is non-recurring congestion caused by incidents such as accidents, disabled vehicles, spilled loads, temporary maintenance and construction activities, signal and detector malfunctions, and other special and unusual events that disrupt the normal flow of traffic. The automated detection of freeway incidents is an important function of a freeway traffic management center. Early detection of incidents is vital for formulating effective response strategies such as timely dispatch of emergency services and incident removal crews, control and routing of traffic around the incident location, and provision of real-time traffic information to motorists. A number of incident detection algorithms, based on conventional approaches, have been developed over the past several decades, and a few of them are being deployed at urban freeway systems in major cities. These conventional algorithms have met with varying degree of success in their detection performance. In this research, a new incident detection technique based on an artificial neural network approach has been proposed. The objective of this research was to demonstrate the use of artificial neural network models for automated detection of lane-blocking incidents on urban freeways. The study focused on the application of neural network models in classifying traffic surveillance data obtained from inductive loop detectors, and the use of the classified output to detect an incident. Three types of neural network models were developed to detect lane-blocking incidents: the multi-layer feed-forward neural network, self-organizing feature map and adaptive resonance theory 2. The models were developed with simulation data from a study site and tested with both simulation and field data at the study site and other locations. The multi-layer feed-forward neural network was found to have the highest potential among the four models to achieve a better incident detection performance. This network consistently detected most of the lane-blocking incidents and gave a false alarm rate lower than the conventional algorithms currently in use. The results have demonstrated the potential of artificial neural network models in improving incident detection performance over currently available techniques.
Hassol, Joshua Lincoln.Automobile
Use, Public Policy and Municipal Government: Factors Influencing the
Implementation of Alternative Transportation Policies. Ph.D., Urban & Regional Planning, 1994. 187 pp. Adviser: Mark Baldassare
The almost exclusive reliance on single-occupant automobiles for intra-urban transportation in the United States has negative social and environmental impacts, including energy use, pollution, and consumption of urban land. Various municipal policies have been proposed to reduce reliance on automobiles. These policies include zoning and land-use changes to promote higher-density residential development and the proximate development of different land uses, pricing policies to make single-occupant automobile use more expensive, and urban design changes intended to make walking, bicycling and transit use easier and more comfortable. Many of the proposed municipal policies run counter to established patterns of urban development and public policy in the United States. Local governments, often with direct or indirect assistance from federal policy, have favored low-density residential development, the segregation of different land uses, and urban design oriented principally toward the requirements of rapid motor vehicle movement. In addition, empirical research indicates that certain demographic factors, in particular income, are positively associated with automobile use. This study tests the hypothesis that the likelihood of any city implementing policies to reduce automobile dependence is inversely related to the degree to which certain physical and demographic characteristics associated with automobile use are manifest in that city. The study collected policy implementation data via a mail survey of municipal planning directors in California. Census data on the population, residential density, growth rate, median household income and other characteristics of each city were attached to the survey data, for statistical analysis. In general, city characteristics do appear to influence policy implementation as hypothesized, although the strength and linearity of the relationships vary among different policies and different city characteristics. City population size, residential density and median income emerge as the strongest predictors of present and likely near-future policy implementation: small, wealthy low-density cities are least likely to implement alternative policies.
The level and extent of demand for a transportation service, including the determinants of the demand, can be meaningfully analyzed only by incorporating their evolution over time. Since most travel demand models are based on cross-sectional data, longitudinal analytic methods need to be developed for the study of travel behavior. Heterogeneity and non-stationarity of behavior, lagged effects, and effect of time varying variables are other factors that require using dynamic modeling techniques. A dynamic beta-logistic model using a panel data set of approximately 2,200 Southern California commuters was developed to fulfill this need. Waves 1, 5, and 8 of this panel, which encompasses a period beginning February, 1990 to February, 1993 was used. Seventy five percent of Waves 1 and 5 data were randomly sampled for model development. The remaining 25 percent as well as the data from Wave 8 were used in model validation. The model had a successful prediction rate of about 98.6% for the two two-wave periods between Waves 1 and 5 and between Waves 5 and 8. Policy simulations were carried with Waves 5 and 8 data. For policy simulation, the impact on ride-sharing of reserved parking, cost subsidy, and guaranteed ride home incentives were studied. An increase of over 100% in the usage of shared-ride mode in Waves 5 and 8 was predicted when all respondents were simulated to have perceived a set of three incentives in both waves. This increase in the shared-ride alternative corresponded to a decrease of over 42% in the usage of the drive-alone modes in both waves. There was a decrease of about 35% in the drive-alone alternative when the three incentives were perceived by all commuters only in Wave 5. If the three incentives were perceived by all commuters in Wave 8 only, the drop in solo-driving in the two-wave period was only 7.1%, which demonstrates the existence of lagged and delayed effects in travel behavior. Of the three incentives guaranteed ride home induces the biggest reduction in the use of the drive-alone alternative.
This study examines the entrepreneurial strategies of African-American and Latino owner-operators in the container hauling sector of the Los Angeles trucking industry. The research proceeded in two stages. In the first, I estimated the ethnic representation of owner-operators and found Latinos to be significantly more represented than other groups. In the second, a snowball sample was used to identify 54 respondents who were interviewed regarding their business behavior and attitudes. The data were analyzed using traditional descriptive statistics as well as multidimensional scaling techniques. The analysis revealed several differences between African-Americans, non-immigrant Latinos, and immigrant Latinos. They differed in the ways they used social networks and co-ethnic support systems. There were more partnerships than expected among African-Americans and more loans and free labor from non-kin co-ethnics for Latinos. Also a higher proportion of immigrants than expected was found among Latinos. The findings of this study lend support to reactive cultural theories and labor market segmentation theories. African-Americans depended heavily on nuclear family partnerships. Both groups were heavily dependent on Latino immigrant labor in the informal sector for employees. A macro analysis suggests that the organization of labor in the harbor is evolving to create greater flexibility in an emerging NIDL (new international division of labor). This study concludes that immigrants out number non-immigrants because they are more flexible about rates and working conditions and not because of a greater tendency to network.
Adler, Jeffrey Lewis.An Interactive Simulation Approach to Systematically Evaluate the Impacts of Real-Time Traffic Condition Information on Driver Behavioral Choice. Ph.D., Engineering, 1993. 210 pp. Adviser: Will Recker
This dissertation proposes a theoretical methodology and practical data collection approach for modeling enroute driver behavior and explaining drivers' decisions to divert and acquire real-time traffic condition information. Limited real-world implementation of Advanced Traveler Information Systems (ATIS) technologies has made it difficult to analyze the potential impact on driver behavior. It is contended here that in-laboratory experimentation with interactive simulation can provide a novel and effective approach to data collection and driver behavior analyses. The theoretical framework is based on conflict assessment and resolution theories and describes changes in enroute behavior as a response to drivers' perceived inability to achieve travel objectives. Conflict is modeled as a latent theoretical concept that describes increased frustration and anxiety experienced by drivers when expected conditions are deteriorating and the desired travel objectives may not be achieved. Motivation to decrease conflict provides the impetus for drivers to adapt enroute behavior by diverting, acquiring additional information, or revising the travel objectives. A case study to examine special event traffic was conducted and several modeling techniques were used to systematically evaluate enroute behavior and the potential impacts of ATIS. Data collection is accomplished through FASTCARS, a computer-based interactive simulation designed to simulate driver decisions and emulate ATIS technologies. Initial empirical results from the analyses are presented to verify the theoretical formulation and modeling strategies.
Many kinds of urban land-use models have been built for varied purposes, depending on a variety of underlying theoretical bases. The general feature of the standard urban economic model is monocentricity rather than polycentricity. However, the contemporary spatial structures of metropolitan areas are too complicated to be described in traditional monocentric terms. In research on polycentric urban models, agglomeration economies are essential in explaining the emergence of subcenters since agglomeration economies are so important for the formation of cities. A time variable is also necessary to present the evolution of metropolitan spatial structures. This dissertation theoretically derives a nonlinear dynamic urban growth model, based on economic location theories of residents, industries and land developers. The model considers the goods, land and labor markets; it also incorporates agglomeration economies, land price and variations in consumer preferences. Population, employment and land price are all determined endogenously and interacted simultaneously. The nonlinear dynamic features of this proposed model make it similar to the urban growth model of Allen and Sanglier (1981), but the proposed model also contains agglomeration economies, land price and economics foundations. The research further investigates the property and features of the derived model by running computer simulations without empirical data. According to the outcomes of the simulations, the model can replicate the result of the monocentric theory, and shows the formation of subcenters given the motive scenarios. The proposed model performs both monocentric term and polycentric term of urban spatial structures depending on different given conditions. It also captures the features of decentralization of population and dispersal of economic activities from central cities to the suburbs. The last part of the dissertation applies the proposed model to data from the Los Angeles region. Results indicate that the model can predict some of the newly emerged centers and rapidly growing zones.
The dissertation seeks to understand how urban commuters adjust their schedules and modes to congestion, as well policy implications of this adjustment. An equilibrium simulation model of commuting traffic on a hypothetical, urban highway corridor is developed. The demand side is a discrete choice model of mode and time of day, estimated with data from the San Francisco Bay Area. The supply side is a speed-flow function that predicts travel time from flows leaving the corridor. The research has three objectives: to simulate the effects of capacity expansion, optimal toll, and six other pricing policies; to test hypotheses relating to schedule shifts in response to congestion and policy changes; and to estimate biases in policy effects when schedule shifts are ignored. An iterative procedure is developed to compute optimal tolls that vary with time of day. Policies are examined from five perspectives: welfare (consumer consumer surplus, toll revenue, and total benefits), peaking (traffic counts and share in the peak 15-minute period), congestion (average and peak 15-minute travel delays), schedule delay (average variable schedule delay), and mode mix (mode shares, average occupancy, and total traffic). Five results emerge. First, although an optimal toll can achieve substantial benefits, savings in travel delay are accompanied by increases in schedule delay. Second, a toll equal to the marginal social externalities of an additional trip at different times of day at a base case can achieve benefits equivalent to those of an optimal toll, which is equal to the marginal social externalities of an additional trip at different times of day at a social optimum. Third, schedule delay has variable and constant components. The constant component is the equilibrium level at a base case when travel is free-flow. The variable component changes with congestion and policies. Fourth, urban commuters shift their schedules in response to congestion and policy changes. Heavy congestion forces people away from the peak; capacity expansion attracts people back to the peak; an optimal toll discourages people driving alone in the peak. Fifth, the benefits of capacity expansion and an optimal toll are substantially overestimated if trip scheduling is ignored.
This is the first study that analyzes two-worker and single-worker households' commuting behavior in the Los Angeles Metropolitan Areas. This study uses 'Excess commuting' to test how important commuting distance is for urban workers to choose their residential and job locations in Los Angeles area. Individual location data used are from the Transit Panel Study Survey, 1991. The results show that commuting distance is still an important factor for urban workers to make location decisions, contrary to other study results. I find that if two-worker households' commuting distance optimization process is restricted by their members job locations, two-worker households' excess commute is smaller than single-worker households'. Also, the results suggest that spatial mismatch restricts unskilled workers in single-worker households more than it restricts workers from other groups. Further, the results show that the commuting distances of two-worker households are affected more by jobs-housing balance in the region than are the commuting distances of single-worker households. I find that two-worker household males behave differently from two-worker household females, and that two-worker household females behave differently from single-worker household females. I also find that there are sharper gender differences among whites than among nonwhites.
One of the most important elements of an effective pavement management system is the collection and interpretation of pavement surface distress data. Current procedures for carrying out this process typically involve on-site visual inspection and condition evaluation by field personnel. This method is a subjective, slow process that is also labor-intensive, tedious and often dangerous. Recent developments in automation of this process have principally been based on the application of machine vision and conventional image processing techniques. Although these developments have considerably advanced the state-of-the-art of automated pavement distress evaluation, their performance has been limited by the inherent shortcomings of conventional image processing techniques applied to pavement images. The objective of this research was therefore to develop and demonstrate the feasibility of an alternative methodology that is based on integration of conventional image processing techniques and artificial neural network models. The research focused on the application of neural network models as pattern classifiers for image interpretation and classification, resulting in the development of neural network-based approaches for automatic thresholding of the images, and for detection and classification of the distresses in each image. Two neural network models were investigated, namely, the multi-layer feed-forward network (MLF) and the 2-stage piecewise linear neural classifier (PLNC). About 250 of the asphalt concrete pavement images acquired by the firm PASCO USA INC. for the US Strategic Highway Research Program (SHRP) were used in this research. The results obtained have shown that the MLF was able to detect and correctly classify about 98% of the images with transverse and longitudinal cracking, and 86% of those with alligator and block cracking. Slightly less impressive results were obtained with the PLNC, although it did perform as well as the MLF in detection of alligator cracking. A method for computation of severity an extent measures has also been presented. These results have clearly demonstrated the potential for application of the neural network-based approach in pavement surface distress evaluation systems, which was the primary objective of this research.
The dissertation examines the spatial patterns of employment and worker residences with three urban density functions: monocentric, polycentric, and omnicentric. Analysis of the 1980 journey-to-work census data for the Los Angeles region reveals that the polycentric density functions statistically predict the actual distributions better than the monocentric density functions. It further shows that the omnicentric density function best predicts the distribution of worker residences. These findings suggest that polycentricity of spatial structure exists in large urban areas, and implies that accessibility to the general employment opportunities is the primary determinant in the residential location choices. The research also investigates urban commuting behavior by estimating the minimum average commute required by these three models. The results show that different urban forms require different amounts of minimum commuting. The standard monocentric model requires a small amount of commute--about one-tenth of the actual commute. The polycentric model predicts the actual commute much better than the monocentric model. Its required commute is about two-fifths of the actual commute, indicating that polycentricity has a positive effect on the estimate of required commute. The omnicentric model best explains the actual commuting patterns among the three models. Its required commute accounts for about 45 percent of the actual commute. These empirical results lead to the conclusion that an urban model better predicting the actual spatial patterns also better explains the actual commuting behavior. This conclusion helps to preserve the assumption that urban workers make attempts to economize on commuting in their location choices. This assumption is implicit in all the three models and implies a positive relationship between the fit of an urban model in explaining the actual distributions and the ability to predict the actual commute. The finding, the standard monocentric model is very poor at explaining the observed urban commute in a major metropolitan area, is more an indictment of the monocentricity assumption than a rejection of the assumption on the commuting behavior. The standard monocentric model greatly underpredicts the actual urban commute because it inadequately represents the actual spatial structure in large metropolitan areas. Relaxing the monocentricity assumption yields better prediction of actual commuting behavior.
Until recently, federal regulation required natural gas pipelines to bundle the sale of natural gas with its transportation. Gas fields connected to city markets through merchant carrier pipelines who bought and sold gas through long-term contracts. Gas buyers were unable to transact directly with gas producers; they were able to deal only through merchant pipelines. This structure nearly precluded gas markets; there were only a few spot markets and there was no futures market. Relaxed pipeline regulation has changed this; natural gas pipelines were permitted to unbundle gas from transportation and to offer pure transportation service. As more pipelines declared themselves to be 'open access' pipelines, spot markets emerged and a futures market opened. Soon pipelines transported far more gas on behalf of their customers than they sold to them. By using and trading transportation on several pipelines, brokers and customers developed the ability to buy and sell gas at many points in the dense transmission grid. When enough pipelines opened themselves to transportation, the connected topology of the network could and did support geographic and intertemporal arbitrage. Monthly and daily spot gas field and citygate prices are examined to determine the extent to which these markets have become integrated. The empirical results show that prices converged and became more cointegrated across the network. The results of a vector autoregression model support the conclusion that by 1990, trading and arbitrage under the new market institutions enforced an equilibrium free of arbitrage opportunities at the field level. At the city market level, the no-arbitrage condition does not yet hold as strongly due to the restrictions placed on transferable transportation rights by state and local authorities. There are still limitations preventing full development of markets and competition in the pipeline network. In light of the dramatic increase in the efficiency of the natural gas market, there is no evidence to support the need for the Federal Energy Regulatory Commission or regulation. Regulation caused the price disparities and allocative inefficiency that markets eliminated.