Abstracts
Dissertations Completed With ITS Support, 1992-2012
2012
Jung, Jaeyoung Shared-ride Passenger Transportation Systems with Real-time Routing PhD, Civil Engineering, 2012. 186 pp Adviser:R. Jayakrishnan.
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.
You, Soyoung Iris Methodology for Tour-based Truck Demand Modeling PhD, Civil Engineering, 2012. 214 pp. Adviser: Stephen G. Ritchie.
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.
2011
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 xxx 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.
2010
Chen, Rex. Broadcasting in Vehicular Ad Hoc Networks PhD, Networked Systems, 2010. 153 pp. Advisers: Amelia Regan and Wenlong Jin
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.
Zheng, Xing. An Adaptive Control Algorithm for Traffic-Actuated Signalized Networks PhD, Civil Engineering, 2010. 150 pp. Adviser: Will Recker
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.
Chung, Albert. Comprehensive Assessment of Managed Lane Performance and Characteristics. PhD, Civil Engineering, 2010. 193 pp. Adviser: Will Recker
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.
Chow, Joseph Y.J. Flexible Management of Transportation Networks under Uncertainty. PhD, Civil Engineering, 2010. 256 pp. Adviser: Amelia C. Regan and R. Jayakrishnan
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.
2009
Sangkapichai, Mana. Transportation and the Environment: Essays on Technology, Infrastructure, and Policy. PhD, Transportation Science, 2009. XX pp. Adviser: Jean-Daniel Saphores.
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.
Park, Ji Young. Network-wide Signal Control with Distributed Real-time Travel Data. PhD, Civil Engineering, 2009. 205 pp. Adviser: R. Jayakrishnan
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.
Jintanakul, Klayut. Dynamic Demand Input Preparation for Planning Applications . PhD, Civil Engineering, 2009. 237 pp. Adviser: R. Jayakrishnan
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.
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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.
2008
Tsai, Mei-Ting. Real Option-based Procurement for Transportation Services. PhD, Civil Engineering, 2008. 143 pp. Advisers: Amelia C. Regan and Jean-Daniel Saphores
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.
Wang, Jiana-Fu. Operational Strategies for Single-Stage Crossdocks. PhD, Transportation Science, 2008. 146 pp. Adviser: Amelia C. Regan
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.
Apivatanagul, Pruttipong. Network Design Formulations, Modeling, and Solution Algorithms for Goods Movement. PhD, Transportation Science, 2008. 180 pp. Adviser: Amelia C. Regan
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.
Tok, Yeow Chern Andre. Commercial Vehicle Classification System using Advanced Inductive Loop Technology. PhD, Civil Engineering, 2008. 177 pp. Adviser: Stephen G. Ritchie
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.
Kim, Hee-Kyung. Activity-based Travel Demand Model with Time-use and Microsimulation Incorporating Intra-household Interactions. PhD, Civil Engineering, 2008. 235 pp. Adviser: Michael G. McNally
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.
Kim, Hyunmyung. New dynamic travel demand modeling methods in advanced data collecting environments. PhD, Civil Engineering, 2008. pp. Adviser: R. Jayakrishnan
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.
Yang, Choon Heon. Developing Decision-Making Process for Prioritizing Potential Alternatives of Truck Management Strategies. PhD Civil Engineering, 2008. 141 pp. Adviser: Amelia C. Regan
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.
2007
Chung, Younshik. Development of Spatio-temporal Accident Impact Estimation Model for Freeway Accident Management. PhD, Civil Engineering, 2007. 277 pp. Adviser: Will Recker
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.
Gan, Liping. A Mathematical Programming Model of Activity Scheduling/Rescheduling in an Uncertain Environment. PhD Civil Engineering, 2007. 223 pp. Adviser: Will Recker
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.
Jeng, Shin-Ting. Real-time Vehicle Re-identification System fo
r Freeway Performance Measurements. PhD, Civil Engineering, 2007. 176 pp. Adviser: Stephen G.
Ritchie
Traffic operations field computational resources as well as the bandwidth of field communication links are o
ften quite limited. Accordingly, for real-time implementation of Advanced Transportation Management and Inf
ormation Systems (ATMIS) strategies, such as vehicle re-identification, there is strong interest in development o
f field-based techniques and models that can perform satisfactorily while minimizing field computational and comm
unication 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 sens
or, 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 c
apable of accurately providing individual vehicle tracking information and performance measurements such as trave
l 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 h
as the potential to be implemented successfully in a congested freeway corridor, utilizing data obtained from bot
h homogenous and heterogeneous loop detection systems. A real-time vehicle classification model, which is b
ased on the PSR approach, was also developed on the part of RTREID-2. The classification model can successf
ully classify vehicles into 15 classes using single loop detector data without any axle explicit information.&nbs
p; 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 i
n this dissertation. A simulation of RTPMS is conducted to evaluate its feasibility. The simulation r
esults 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 le
vel of vehicular emissions. However, in current practice, travel forecasting models rely on steady state hourly
averages and are thus incapable of accurately capturing the effects of network traffic variations accurately on e
missions. Recent research has focused on the implementation of modal emission models to overcome some of these sh
ortcomings in existing emission rate models. A primary input to modal emission models is the fraction of time spe
nt in different driving patterns. The estimation accuracy, however, is hampered by the application of static trav
el 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 m
odels 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 con
sidered in this research includes geometric design elements, traffic characteristics, roadside environment charac
teristics, 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 struct
ural 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 t
raffic 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 accura
tely. Models that estimate time-dependent emissions in the presence of traffic sensor data were also successfully
estimated.
2006
Girvin, Raquel. Economic Analysis of Aircraft and Airport Noise Regulations. Ph.D, Transportation Science, 2006. 151 pp. Adviser: Jan Brueckner.
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.
Duan, Junping. Similarity Analysis for Estimation of an Activity-based Travel Demand Model. Ph. D, Civil Engineering, 2006. 200 pp. Adviser: Will Recker.
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.
McGowen, Pat . Predicting Activity Types from GPS and GIS Data. Ph. D, Civil Engineering, 2006. 144 pp. Adviser: Mike McNally.
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.
Nandiraju, Srinivas. Strategic Freight Transportation Contract Procurement. Ph.D., Civil Engineering, 2006. 285 pp. Adviser: Amelia C. Regan.
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.
Pages, Laia. Real Time Mass Transport Vehicle Routing Problem: Hierarchical Global Optimization for Large Networks. Ph.D., Civil Engineering, 2006. pp. Adviser: R. Jayakrishnan
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.
2005
Park, Minyoung. Capacity Modeling for Multimodal Freight Transportation Networks. Ph.D., Civil Engineering, 2005. 139 pp. Adviser: Amelia C. Regan.
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.
2004
Park, Seri. Vehicle Monitoring for Traffic Surveillance and Performance using Multi-Sensor Data Fusion. Ph.D., Civil Engineering, 2004. 225 pp. Adviser: Stephen G. Ritchie
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.
Steimetz, Seiji S.C. New Methods for Modeling and Estimating the Social Costs of Motor Vehicle Use. Ph.D., Economics, 2004. Advisers: David Brownstone and Kenneth A. Small.
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.
2003
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.
Oh, Cheol. Anonymous Vehicle Tracking for Real-time Traffic Performance Measures. Ph.D., Civil Engineering, 2003. 193 pp. Adviser: Stephen G. Ritchie.
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.
Pavlis, Ioannis. Logic-Based Modeling and Solution of a Linear Optimal Signal Control Problem for Surface Street Networks. Ph.D., Civil Engineering, 2003. 417 pp. Adviser: Will Recker.
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.
Rindt, Craig. The Tractability and Performance of Microsimulating Human Activity for Transportation Systems Analysis Ph.D., Civil Engineering, 2003. 160 pp. Adviser: Michael G. McNally.
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.
Song, Jiongjiong. Combinatorial Auctions: Applications in Freight Transportation Contract Procurement. Ph.D., Civil Engineering, 2003. 141 pp. Adviser: Amelia C. Regan.
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.
Wei, He (Helen). Two Essays on Economics with Applications in Hypercongestion and Bus Transit. Ph.D., Economics, 2003. 114 pp. Adviser: Kenneth A. Small.
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.
Yang, Xu. Assessment of a Self-Organizing Distributed Traffic Information System: Modeling and Simulation. Ph.D., Civil Engineering, 2003. 225 pp. Adviser: Will Recker.
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.
2002
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.
Kulkarni, Anup. Modeling Activity Pattern Generation and Execution. Ph.D., Transportation Science, 2002. 143 pp. Adviser: Michael G. McNally.
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.
Marca, James Edward. Activity-based Travel Analysis in the Wireless Information Age. Ph.D., Civil Engineering, 2002. 162 pp. Adviser: Michael G. McNally.
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.
2001
Ghosh, Arindam. Valuing Time and Reliability: Commuters' Mode Choice from a Real Time Congestion Pricing Experiment. Ph.D., Economics, 2001. 152 pp. Adviser: David Brownstone.
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.
Lee, Ming-Sheng. Experiments with a Computerized Self-Administrative Activity Survey. Ph.D., Civil Engineering, 2001. 175 pp. Adviser: Michael G. McNally.
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.
Lu, Xiangwen. Dynamic and Stochastic Routing Optimization: Algorithm Development and Analysis. Ph.D., Civil Engineering, 2001. 139 pp. Adviser: Amelia C. Regan.
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.
Oh, Jun-Seok. Dynamic Route Guidance and Network Traffic Management: Theoretical Evaluation and Practical Application Issues. Ph.D., Civil Engineering, 2001. 284 pp. Adviser: R. Jayakrishnan.
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.
Wang, Xiubin. Algorithms and Strategies for Dynamic Carrier Fleet Operations: Applications to Local Trucking Operations. Ph.D., Civil Engineering, 2001. 104 pp. Adviser: Amelia C. Regan
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.
2000
Aydogan, Neslihan. Essays on the Economics of Industry Location, Innovation and Firm Organization. Ph.D., Economics, 2000. 84 pp. Advisers: Kenneth A. Small and John Di Nardo
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.
Lam, Terence Chonchoi. The Effect of Variability of Travel Time on Route and Time-of-Day Choice. Ph.D., Economics, 2000. 174 pp. Adviser: Kenneth A. Small
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.
1999
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.
Parkany, Ann Emily. Traveler Responses to New Choices: Toll Versus Free Alternatives in a Congested Corridor. Ph.D., Transportation Science, 1999. 168 pp. Adviser: Kenneth A. Small
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.
Sheng, Hongyan. A Dynamic Household Alternative-Fuel Vehicle Demand Model using Stated and Revealed Transaction Information. Ph.D., Economics, 1999. 102 pp. Adviser: David Brownstone.
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.
1998
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).
Yu, Xiao-Hua (Helen). Markovian Decision Control for Traffic Signal Systems. Ph.D., Electrical & Computer Engineering, 1998. 105 pp. Adviser: Allen R. Stubberud
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.
1997
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.
Ryan, Sherry. The Value of Access to Highways and Light Rail Transit: Evidence for Industrial and Office Firms. Ph.D., Transportation Science, 1997. 138 pp. Adviser: Joseph F. DiMento
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.
Sheu, Jiuh- Biing. Stochastic Estimation of Lane-Changing Probabilities and Its Application to Incident Detection. Ph.D., Civil Engineering, 1997. 237 pp. Adviser: Stephen G. Ritchie
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.
Wey, Wann-Ming. A Network Traffic Control Algorithm with Analytically Embedded Traffic Flow Models. Ph.D., Civil Engineering, 1997. 167 pp. Adviser: R. Jayakrishnan
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.
1996
Abdulhai, Baher. A Neuro-Genetic-Based Universally Transferable Freeway Incident Detection Framework. Ph.D., Civil Engineering, 1996. 171 pp. Adviser: Stephen G. Ritchie
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.
Chen, Chienho.An Activity-Based Approach to Accessibility. Ph.D., Civil Engineering, 1996. 144 pp. Adviser: Will Recker
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.
Koskenoja, Pia Maria K. The Effect of Unreliable Commuting Time on Commuter Preferences. Ph.D., Economics, 1996. 242 pp. Adviser: Kenneth A. Small
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.
Nolan, James Francis.Efficiency in the Bus Industry: Measurement and Identification of Performance Determinants. Ph.D., Economics, 1996. 136 pp. Advisers: Gordon J. Fielding and David Brownstone
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.
Wang, Ruey-Min.An Activity-Based Trip Generation Model. Ph.D., Civil Engineering, 1966. 234 pp. Adviser: Michael G. McNally
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.
1995
Crepeau, Richard Joseph. Mobility and the Metropolis: Issues of Travel and Land Use in Urban America. Ph.D., Urban & Regional Planning, 1995. 143 pp. Adviser: Randall Crane
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.
Kazimi, Camilla. A Microsimulation Model for Evaluating the Environmental Impact of Alternative-Fuel Vehicles. Ph.D., Economics, 1995. 207 pp. Adviser: David Brownstone
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.
Khan, Sarosh Islam. Modular Neural Network Architecture for Detection of Operational Problems on Urban Arterials. Ph.D., Engineering, 1995. 112 pp. Adviser: Stephen G. Ritchie
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.
Sarmiento, Sharon Maria S. Studies in Transportation and Residential Mobility. Ph.D., Economics, 1995. 159 pp. Adviser: David Brownstone
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.
1994
Cheu, Ruey Long.Neural Network Models for Automated Detection of Lane-Blocking Incidents on Freeways. Ph.D., Engineering, 1994. 197 pp. Adviser: Stephen G. Ritchie
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.
Khanal, Mandar.Dynamic Discrete Demand Modeling of Commuter Behavior. Ph.D., Engineering, 1994. 140 pp. Adviser: Will Recker
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.
Smith, James Edward. A
Comparative Study of Entrepreneurial Strategies among African American
and Latino Truckers in the Los Angeles and Long Beach Ports. Ph.D., Sociology, 1994. 358 pp.
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.
1993
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.
Chen, Hsin-Ping.Theoretical Derivation and Simulation of a Nonlinear Dynamic Urban Growth Model. Ph.D., Economics, 1993. 270 pp. Adviser: Kenneth A. Small
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.
Chu, Xuehao. Trip Scheduling and Economic Analysis of Transportation Policies. Ph.D., Economics, 1993. 252 pp. Advisers: Gordon J. Fielding and Kenneth A. Small
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.
Kim, Seyoung. Commuting Behavior of Two-Worker Households in the Los Angeles Metropolitan Area. Ph.D., Economics, 1993. 118 pp. Adviser: David Brownstone
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.
1992
Kaseko, Mohamed Said.A Neural Network-Based Methodology for Automated Distress Classification of Highway Pavement Images. Ph.D., Engineering, 1992. 159 pp. Adviser: Stephen G. Ritchie
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.
Song, Shunfeng. Spatial Structure and Urban Commuting.Ph.D., Economics, 1992. 116 pp. Adviser: Kenneth A. Small
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.
Walls, William David. Open Access Transportation, Network Competition, and Market Integration in the Natural Gas Pipeline Industry. Ph.D., Economics, 1992. 165 pp. Adviser: Arthur S. De Vany
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.