Dissertations Completed With ITS Support, 1992-2009

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.

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:

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.