Event Type: PhD Defense

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.

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.

Variations in traffic volumes and changes in travel-related characteristics significantly contribute to the level of vehicular emissions. However, in current practice, travel forecasting models rely on steady state hourly averages and are thus incapable of accurately capturing the effects of network traffic variations accurately on emissions. Recent research has focused on the implementation of modal emission models to overcome some of these shortcomings in existing emission rate models. A primary input to modal emission models is the fraction of time spent in different driving patterns. The estimation accuracy, however, is hampered by the application of static travel demand models for predicting driving patterns. There is a real need to evolve alternate methods to accurately predict driving patterns.

This dissertation proposes an approach to predicting driving patterns more accurately by applying different models at the macroscopic and microscopic network levels. The proposed models more accurately estimate the driving pattern by considering a set of Emission Specific Characteristics (ESC) for each network link. Specific ESC considered in this research includes geometric design elements, traffic characteristics, roadside environment characteristics, and driver behavior.

Two different models have been developed in this study to capture the driving patterns at each network level. The first model is designed to capture macro-scale driving patterns (average speed) in a larger network and the second model is designed to capture micro-scale driving patterns. The two models have been developed using structural equations. They have been calibrated, evaluated, and validated using a microscopic traffic simulation model. Analysis of the models reveals that geometric design elements exert greater influence on driving patterns than traffic characteristics, roadway environment characteristics, and driver behavior in the estimation of emissions. This research has concluded that, for congested traffic conditions, the proposed models capture driving patterns more accurately than current practice and, consequently, these models estimate the range of emissions more accurately. Models that estimate time-dependent emissions in the presence of traffic sensor data were also successfully estimated.

Traffic operations field computational resources as well as the bandwidth of field communication links are often quite limited. Accordingly, for real-time implementation of Advanced Transportation Management and Information Systems (ATMIS) strategies, such as vehicle re-identification, there is strong interest in development of field-based techniques and models that can perform satisfactorily while minimizing field computational and communication requirements. The ILD (Inductive Loop Detector)-based Vehicle ReIDentification system (ILD-VReID) is an example of a currently applied approach. Although ILDs are not without limitations as a traffic sensor, they are widely used for historical reasons and the sunken investment in the large installed base makes their use in this research highly cost-effective. Therefore, this dissertation develops a new vehicle re-identification algorithm, RTREID-2, for real-time implementation by adopting a PSR (Piecewise Slope Rate) approach that extracts features from raw vehicle signature data. The results of cases studies indicate that RTREID-2 is capable of accurately providing individual vehicle tracking information and performance measurements such as travel time and speed. The potential contributions of RTREID-2 are: application to square and round single loop configurations, and reduced computational requirements associated with re-estimation or transferability of the speed models used in the previously developed approach. As a consequence RTREID-2 is obviated for site-specific calibration and transferability issues. A freeway corridor study also demonstrates that RTREID-2 has the potential to be implemented successfully in a congested freeway corridor, utilizing data obtained from both homogenous and heterogeneous loop detection systems. A real-time vehicle classification model, which is based on the PSR approach, was also developed on the part of RTREID-2. The classification model can successfully classify vehicles into 15 classes using single loop detector data without any axle explicit information. The initial results also suggest the potential for transferability of the vehicle classification approach and are very encouraging. To investigate real-time freeway performance measurement in a real-world setting, the design of RTPMS (Real-time Traffic Performance Measurement System) that is based on RTREID-2 is also presented in this dissertation. A simulation of RTPMS is conducted to evaluate its feasibility. The simulation results demonstrate the potential of implementing RTPMS in real world application.

The objective of this dissertation is to develop and apply an analytic procedure that estimates the amount of traffic congestion (vehicle hours of delay) that is caused by different types of accidents that occur on urban freeways, as well as to develop a model for prediction of real-time accident information such as how long an accident will affect traffic congestion and to the extent of the traffic congestion. Although it has been speculated that non-recurrent congestion caused by accidents, disabled vehicles, spills, weather events, and visual distractions accounts for one-half to three-fourths of the total congestion on metropolitan freeways, there are insufficient data to either confirm or deny this conjecture. 
 
The first part of this dissertation develops a method to separate the non-recurrent delay from any recurrent delay that is present on the road at the time and place of a reported accident, in order to estimate the contribution of non-recurrent delay caused by the specific accident. The procedure provides a foundation for a forecasting model that will assist transportation agencies such as Caltrans to allocate resources in the most effective way to mitigate the effects of those accidents that are likely to result in the greatest amount of delay. Additionally, since freeway travelers may be able to alter their driving routes based on the real-time accident information, the forecasting model may reduce traffic congestion and the incidence of secondary accidents.
 
Since a number of estimated delay results were censored by time and/or space boundary conditions, general statistical approaches were not available. An approach based on survival analysis was applied to analyze estimated delay and to predict traffic congestion impact in terms of time and space. Specifically, a statistical model based on the Cox type proportional hazard analysis is estimated that describes non-recurrent delay as a function of day of week, time of day, weather, and observable (e.g., from emergency calls and/or aerial or on-scene observation) characteristics of the accident. These accident characteristics, which are available to Freeway Traffic Management Systems, include time of day, number of involved vehicles, whether a truck is involved, and collision location (by lane or side of road). This statistical model can be used to inform a manager as to the expected delay associated with an accident as soon as the accident is reported and its characteristics are observed. This can in turn be used in improving resource allocation.
 
Additionally, this dissertation develops three prediction models regarding the spatio-temporal impact caused by a traffic accident as well as an accident duration model based on AFT metric model. Information provided by such predictions can play an important role in public sector transportation agencies providing freeway travelers with real-time traffic information under incident conditions.

The so-called activity-based approach to analysis of human interaction within social and physical environments dates back to the original time-space geography works of Hägerstrand and his colleagues at the Lund School in 1970, with a unique kernel problem termed “household activity scheduling”. The problem attempts to derive estimates of activity decisions taking into account the time, duration, mode, location and route of the given activity sets performed by individuals. 
This dissertation research studies the activity scheduling/rescheduling problem under an uncertain environment. Theories and models for predicting activity-travel behavior are developed within the context of an activity-based approach built on the general consensus that the demand for travel is derived from a need or desire to participate in activities. Computationally-tractable systems are developed that inherently incorporate factors of uncertainty that can potentially increase the ability to address the household activity scheduling problem and the related dynamics of human movement required for social interaction and household sustenance. A stochastic mixed integer linear program is formulated to model travel behavior in which each activity of the prescribed household agenda has a known probability of being completed (or cancelled). Further, a chance-constrained program is proposed to determine the optimal activity/travel pattern when travel time and activity duration are assumed to be stochastically distributed, while the remaining inputs are precisely known. Finally, under the assumption that the activity/travel pattern involves a dynamic decision-making process of rescheduling/adaptations to initial plans subject to unexpected events, a predictive model of activity rescheduling behavior is developed in the form of a mixed integer linear program. 
The dissertation presents solution methodologies to the proposed models. Data drawn from a comprehensive on-line survey are utilized to verify the proposed activity schedule/reschedule models. Numerical results are presented to demonstrate the performance of the proposed models. Finally, conclusions and directions for future research are summarized. 

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, the per-aircraft limit offers only modest welfare gains above the levels achieved with the cumulative limit, whereas the cumulative limit substantially increases welfare above that achieved with the per-aircraft limit in some regions of the parameter space explored. The effects of noise taxation and the optimal level of noise taxes are also investigated with the duopoly model; the analysis shows equivalence between noise taxation and the cumulative noise limit. 

Within the existing body of activity scheduling behavior models, the Household Activity Pattern Problem (HAPP) model is an activity-based model characterized by a rigorous mathematical programming formulation. The HAPP model can deal with detailed activity patterns including spatial, temporal, personal and modal information with complex constraints. The HAPP model is in the form of a Mixed Integer Programming model (MIP) which includes both continuous variables and discrete variables. Such temporal attributes of an activity pattern as starting time, duration and ending time are continuous variables, and those spatial attributes associated with the sequencing of activities, travel modes, participation persons and vehicles are discrete variables.
As formulated, the HAPP model is a constrained utility maximizing model. Empirical application of the model to a demand context involves estimation of the components of the objective function, based on data from observed patterns. However, due to computational difficulties in HAPP model, genetic algorithms (GA) have been proposed to estimate the set of factors influencing the objective function that “best” reproduces the observed spatial and temporal interrelationships. The fitness score in the GA approach used to evaluate the quality of the representation is the difference between the observed activity pattern scheduling (OAPS) and predicted activity pattern scheduling (PAPS), or the similarity between the two.
In this dissertation, we propose a new similarity metric for the GA estimation procedure. The metric considers the problem based on the continuous representation of discrete activity variables along the temporal dimension. Three similarity judging rules work together to form the similarity definition of similarity metric. They are: the temporal overlap among activities of different type, correspondence between participant person and vehicle used for each activity; permutations in the temporal sequence of activities and activity duration length similarity. The estimation procedure is tested on data drawn from a well-know activity/travel survey.

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.

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.