Event Type: PhD Defense

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 in the freight transportation planning process, particularly from a multimodal perspective. The multimodal network capacity model formulated 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 formulation, referred to as the MNCP model in this dissertation, 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 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 proposed MNCP model in articulating investment priorities across all transportation modes, thus achieving their goal of developing sustainable transportation systems in a cost-effective manner. 

Advances in traffic surveillance technology can provide more complete and intelligent data from detectors. This dissertation describes an improved method of freeway performance measurement that integrates multi-sensor data fusion with a vehicle-monitoring algorithm capable of identifying the same vehicle/s at different locations. To obtain a more robust and effective data set for vehicle monitoring, data fusion from two state–of–the-art traffic detectors — loop detectors and video detectors — was introduced. Investigations and development of a new algorithm for data fusion and real-time vehicle monitoring – TRASURF (TRAffic SURveillance and perFormance) were also described. The algorithm’s development was based on an examination of feature vector extraction from each advanced traffic sensor, data fusion across multiple technologies and analysis of sensor performance. A real-world data set from one section of the I-405 freeway was applied to develop and evaluate the algorithm for a single freeway section. Based on extensive analysis of these field data, the PARAMICS (PARAllel MICroscopic Simulation) model was used to generate simulated fused data. This simulation served as the means to test and evaluate the performance of TRASURF as a multi-section vehicle-monitoring algorithm. The algorithm’s ability to reconstruct individual vehicle trajectories will enable more efficient and effective traffic surveillance, and will enhance the collection and analysis of network-wide traffic information including path travel time and origin-destination matrices. Furthermore, investigations and descriptions of various applications of advanced detectors for traffic analysis, especially in the context of the single-loop configuration widely used within California and many other locations were made. Traffic data extraction based on advanced loop detectors will make a vital contribution to many aspects of traffic operations and management, as these data are not available from conventional detectors.

A fundamental requirement for the successful implementation of advanced transportation management and information systems (ATMIS) is the development of a real-time traffic surveillance system that can produce accurate and reliable traffic performance measures. My dissertation presents a new framework for anonymous vehicle tracking that is capable of tracking individual vehicles using vehicle features. The core of the proposed method is a vehicle reidentification algorithm for signalized intersections based on inductive vehicle signatures. This has 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. I developed an evaluation framework for vehicle tracking performance using a microscopic traffic simulation. I conducted a systematic simulation investigation of the performance and feasibility of anonymous vehicle tracking along multiple detector stations using the proposed simulation evaluation framework. The proposed system produces a rich data source for OD estimation. This application is the focus of my dissertation. I also present other useful applications of inductive vehicle signatures. These include the development of a methodology for evaluating traffic safety based on individual vehicle information and the prediction of section travel times. The proposed methodology can also be a valuable tool for operating agencies to support a number of intelligent transportation systems (ITS) strategies including congestion monitoring, adaptive traffic control, system evaluation and provision of real-time traveler information.

The real-life traffic flow process of signalized surface street networks
can be naturally decomposed into the traffic flow and the control strategy
components. The first challenge is faced when modeling the traffic flow
process on the surface streets. Specifically, the mathematical
description of traffic dynamics can be obtained by a number of models that
have a direct effect on the complexity of the corresponding optimal
control problem. Moreover, in order for the mathematical model to be
considered as part of a constraint optimization problem the constraints
must be expressed by inequalities (linear or not). However, the
consistent mathematical description of the traffic flow process inevitably
includes conditional piece-wise functions. For example, the traffic flow
at the approach of a signalized intersection is a piece-wise function
whose range depends (is conditional) on the prevailing traffic conditions
and the signal indication. Expressing this function (or others of similar
form) as a set of constraints that are additionally linear with respect to
the corresponding variables is a non-trivial task. The practices
typically followed include ignoring this function by averaging the outflow
during green over the cycle length, or approximating it with inexact
representations, or manipulating it during the solution process. These
approaches result to modeling inconsistencies and solutions of
questionable quality due to the involved heuristics. On the other hand,
there are cases of such functions that are equivalently represented by a
Mixed Integer Model (MIM) i.e., a set of inequality constraints in both
continuous and discrete variables.
The next challenge appears in designing the control strategy model.
Surprisingly, while someone would expect the modeling of the control
strategies to be driven by the currently followed state-of-the-practice,
one discovers that even the most recent modeling approaches follow the
outmoded concept of single-ring controllers with cycles of fixed duration
for a single pair of conflicting movements. Moreover, despite the fact
that the aforementioned control strategy (or its variations) is the most
widely adopted and optimized over surface street networks of comparable
dimensions, the corresponding solution time that offers a qualitative
measure of the strategy performance is not reported. Similarly, for those
control strategy models that are solved as Mixed Integer Programming
Problems no relative information on the solution time is provided.
 
The general objective of this dissertation is to address the issues
associated with developing a model for signalized surface street networks
and solving the corresponding optimal control problem. In order to
accomplish this goal the following specific aims are fulfilled:
1.   Based on analogies from the theory of mathematical logic we develop two
methodologies for transforming conditional piece-wise functions into an
equivalent MIM representation.
2.   We demonstrate the potential of both methodologies by their application
to a number of conditional piece-wise functions that are found during the
process of developing a mathematical representation for signalized surface
street networks that is based either on the dispersion-and-storage or on
the cell transmission traffic flow models. For example, we have developed
MIM representations that describe the cases of the outflow at the approach
of a signalized intersection when assuming a 2-band (Green-Red) signal, or
when assuming a 3-band (Green-Yellow-Red) signal, etc.
3.   We demonstrate the capability of both methodologies in analyzing the
structure of existing MIMs, which subsequently enables us to provide
improved (in terms of the variables and the constraints) representations.
4.   We develop a control strategy model that describes a dual-ring,
8-phase, variable cycle controller, and we further propose an alternative
formulation that is based again on logic-based methods that could
potentially be useful (in terms of the solution time of the corresponding
problem) within the context of a customized branch-and-bound solution
algorithm.
5.   We examine the performance of our control strategy under various
hypotheses both quantitatively (solution time) and qualitatively using the
CPLEX solver that is based on a branch-and-cut solution algorithm.
 
This dissertation demonstrates the potential of the optimal control
approach as a powerfull tool in solving the complex problem of optimizing
traffic signals for surface street networks.

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 the modeling flexibility needed 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, I describe a microsimulation approach that focuses on the activity execution process. This bottom-up approach presents a problem of tractability. I solve 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, I describe a complete agent-based model of an urban activity system 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, I present results from experiments using candidate adaptive learning algorithms for agents operating in the microsimulation to demonstrate the utility of the approach.

This dissertation is an initial feasibility study of a self-organized, 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 on predictions provided by mathematical formulations and testing with microscopic traffic simulations. The research in this dissertation focuses on the traffic aspects of the proposed Autonet, using simulation approaches to assess the potential benefits that might be accrued by the traffic system and to evaluate the ability of the computing overlay to handle the traffic “application”. 
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 programing 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-organized, 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 the proposed systems.
Further, a series of vehicle re-routing applications under the incident condition based upon the proposed self-organized, distributed information system are tested via simulation. 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.

This dissertation focuses on the bidding problem in combinatorial auctions with particular application to the freight transportation contract procurement problem. Combinatorial auctions are auctions in which a set of heterogeneous items are sold simultaneously and in which bidders can bid for combinations of items. These auctions involve many difficult problems for both auctioneers and bidders and have received recent attention from computer scientists, operations researchers and economists. Large shippers have begun to use this method to procure services from trucking companies by selling contracts for delivery routes.  

This dissertation analyzes the impact of the combinatorial auction procurement method on both shippers and carriers. I demonstrate that bidders have more complicated optimization problems than auctioneers in combinatorial auctions. The bid construction problem, i.e. how bidders identify and construct beneficial bids, is very difficult to solve and remains an open question. This dissertation investigates this problem and proposes an optimization-based approximation method that involves solving an NP-hard problem a single time, yielding significant improvements in computational efficiency.

I also examine the current state of the trucking and third party logistics industries. The trucking industry is very competitive and small carriers are operating under thin margins. This dissertation addresses this issue by proposing an auction-based collaborative carrier network in which participating carriers identify inefficient lanes and exchange them with partners. This is shown to be Pareto efficient. I then discuss decision problems related to how carriers identify inefficient operations and make and select bids. This represents an effort to utilize an advanced auction mechanism to enhance carriers’ operational efficiencies in e-commerce.

This dissertation research proposes the development and evaluation of a new concept for high-coverage point-to-point transit systems (HCPPT). The system is a radically new operational scheme for mass transit that relies on a large number of small transit vehicles routed using advanced communication technology and real-time stochastic control algorithms. The defense presentation covers the three major contributions of this research. First, the details of the concept are described, outlining its features and the flexibility of potential operational schemes. Second, the real-time routing algorithms are presented. 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, some numerical results obtained from a simulated case study in Orange County are shown. This study was carried out using a multi-purpose simulation platform developed in the context of this research.  The final simulations 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 brief demonstration (animation) of the simulation tool applied to the HCPPT scheme is also included in the presentation.

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 highway with the presence of spillovers. Second, to shed more light on the spatial detail of economic spillovers, empirical tests of the spillovers hypothesis are conducted at the metropolitan level, with census tracts as the unit of observation. The results of the quasi-experiment reveal the employment growth patterns of census tracts in Los Angeles County that confirms the existence of negative spillovers, caused by the opening of the Interstate 105 in 1995. The benefiting area, which grew substantially after the highway was opened, is limited to a long narrow highway corridor while nearby locations outside the corridor experience 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 the regional governing bodies should pay special attention to the distributional impact of highway projects.

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 adds the use of wireless communications technology to
integrate 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 goal 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. 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.