Abstract

This dissertation is composed of three chapters on estimation of vehicle choice and utilization models, simulated likelihood estimation, and Bayesian non-parametric additive methods for neighborhood effect models. The first chapter exploits differences in fuel efficiency between hybrid vehicles and their gasoline counterparts to investigate two behavioral questions relating to fuel economy standards: how car buyers value fuel economy (the energy paradox) and whether improved fuel efficiency increases travel (the rebound effect). Emphasis is placed on handling methodological and data issues that are typically ignored in prior studies, such as partially observed choice, endogeneity, and measurement error. Estimates of the rebound effect and consumer valuation of fuel economy remain imprecise despite the use of the most detailed household level data available and sound methodology to handle limitations with these data. The inability to precisely estimate these important policy questions suggests it is a worthwhile endeavor to obtain reliable, detailed data on household vehicles. The following chapter (joint with Ivan Jeliazkov) presents techniques, based on Markov chain Monte Carlo (MCMC) theory, for construction of the likelihood function in a broad class of hierarchical models where direct evaluation of the likelihood function is not possible. We review existing estimators, introduce new MCMC estimators, and examine their performance in applications to the Poisson-log normal and mixed logit models. The MCMC techniques outperform existing methods in both settings, with the existing methods performing especially poorly in the Poisson-log normal case. The final chapter applies Bayesian semiparametric additive methods to a neighborhood effects model. The baseline model assumes all covariates enter linearly, whereas the approach in this paper allows for flexible functional forms. An efficient Markov chain Monte Carlo (MCMC) algorithm that exploits the properties of banded matrices is proposed for estimation. The efficiency gains offered by the banded matrix algorithm are critical, as they permit the estimation of applications with large sample sizes. The model and estimation methodology are used to examine foreclosure contagion in California. The results reveal the impact of neighborhood effects on foreclosure rates as nonlinear, where the relationship resembles a tipping point phenomenon.

Abstract

The purpose of my dissertation is to explore how gender interacts with other factors such as personal attitudes, earning power, household structure, and the built environment to influence travel behavior, with a focus on whether these factors strengthen or relieve the constraints women face when making travel choices. In this context, my dissertation is organized around three separate case studies in California that rely on various discrete choice econometric models. Results from my first case study indicate that chauffeuring trips in two-adult households with children are intensely gendered, and women bear most of the chauffeuring burden. It is partly because women’s income earning potential is generally lower than that of their male partners. However, living in neighborhoods with access to bus stop and with less single-family housing can reduce this gender chauffeuring gap. It suggests that compact urban development and better bus service may yield social benefits that help alleviate women’s household burdens. In my second case study, I find that mothers are more likely to extend their greater concerns about traffic safety to their children, which in turn reduces the chance that their children will walk or bike to school. However, mothers bear most of the burden to chauffeur their children to school not because they worry more, but because chauffeuring children is still seen more as a mother’s responsibility. It suggests that interventions targeting an increase in children’s active commuting to school should focus on the concerns of mothers, especially as they relate to traffic characteristics. My findings in the third case study reveal that both environmental and safety concerns are associated with sustainable travel behavior, but the influence of safety concerns is more prominent and women have greater safety concerns. Moreover, proximity to transit service can increase sustainable travel behavior, but having higher safety concerns can totally offset this effect. For women with higher safety concerns, the reduction is even greater. It suggests that to encourage sustainable travel behavior, reducing personal safety concerns about transit use may be more effective than increasing public environmental awareness, especially for attracting potential female riders.

Abstract

Recent research has indicated convincing evidence of a link between characteristics of the built environment and travel behavior. However, few land use – travel behavior studies include cognitive factors (such as attitudes, perceptions, and environmental norms) that have been found to affect travel mode choice in the social psychology literature. This dissertation develops and empirically tests a theoretical framework called the Perception-Intention-Adaptation (PIA) model that brings land use and attitude-behavior theory together in order to address gaps in the travel behavior literature. Following a detailed description of the PIA model, the dissertation is comprised of three empirical essays. The analyses in these essays are based on cross-sectional and panel data collected during the Expo Line Study, the first experimental-control, before-and-after evaluation of a rail transit investment in California. The first essay evaluates the predictive power of the core socio-psychological constructs of the PIA (attitudes, norms, and control beliefs) in combination with a comprehensive set of built environment and socio-economic measures. Regression models of transit use are used to analyze cross-sectional data obtained before the opening of the Exposition light rail line in Los Angeles. The analysis indicates that two PIA constructs, attitudes toward public transportation and concerns about personal safety, significantly improve the model fit and were robust predictors of transit use, independent of built environment factors. The second essay uses panel data collected before and after the opening of the Exposition light rail line to examine changes in travel behavior. A quasi-experimental approach with experimental (within ℗ư mile of an Expo station) and control (beyond ℗ư mile) households is used to evaluate the travel effects of the opening of the Expo line at the household level. The results show a statistically significant reduction in vehicle miles traveled (VMT) in the experimental group, though overall transit ridership and travel-related physical activity did not change significantly. The final essay uses the before and after opening panel data to examine socio-psychological aspects of travel behavior change in response to the Expo Line opening. Random effects models of transit use, car driver trips, and active travel trips all show that the socio-psychological constructs hypothesized in the PIA model do have a significant impact on travel behavior. In addition, cross-lagged models designed to examine the attitude-behavior relationship show an apparent causal pathway from attitudes to behavior for all three travel outcomes.

Abstract

This dissertation focuses on the integration of the Household Activity Pattern Problem (HAPP) with various locational decisions considering both supply and demand sides. We present several methods to merge these two distinct areas—transportation infrastructure and travel demand procedures—into an integrated framework that has been previously exogenously linked by feedback or equilibrium processes. From the demand side, travel demand for non-primary activities is derived from the destination choices that a traveler makes that minimizes travel disutility within the context of considerations of daily scheduling and routing. From the supply side, the network decisions are determined as an integral function of travel demand rather than a given fixed OD matrix.

First, the Location Selection Problem for the Household Activity Pattern Problem (LSP-HAPP) is developed. LSP-HAPP extends the HAPP by adding the capability to make destination choices simultaneously with other travel decisions of household activity allocation, activity sequence, and departure time. Instead of giving a set of pre-fixed activity locations to visit, LSPxviii HAPP chooses the location for certain activity types given a set of candidate locations. A dynamic programming algorithm is adopted and further developed for LSP-HAPP in order to deal with the choices among a sizable number of candidate locations within the HAPP modeling structure. Potential applications of synthetic pattern generation based on LSP-HAPP formulation are also presented.

Second, the Location – Household Activity Pattern Problem (Location-HAPP), a facility location problem with full-day scheduling and routing considerations is developed. This is in the category of Location-Routing Problems (LRPs), where the decisions of facility location models are influenced by possible vehicle routings. Location-HAPP takes the set covering model as a location strategy, and HAPP as the scheduling and routing tool. The proposed formulation isolates each vehicle’s routing problem from those of other vehicles and from the master set covering problem. A modified column generation that uses a search method to find a column with a negative reduced price is proposed.

Third, the Network Design Problem is integrated with the Household Activity Pattern Problem (NDP-HAPP) as a bilevel optimization problem. The bilevel structure includes an upper level network design while the lower level includes a set of disaggregate household itinerary optimization problems, posed as HAPP or LSP-HAPP. The output of upper level NDP (level-ofservice of the transportation network) becomes input data for the lower level HAPP that generates travel demand which becomes the input for the NDP. This is advantageous over the conventional NDP that outputs the best set of links to invest in, given an assumed OD matrix. Because the proposed NDP-HAPP can output the same best set of links, a new OD matrix and a detailed temporal distribution of activity participation and travel are created. A decomposed xix heuristic solution algorithm that represents each decision makers’ rationale shows optimality gaps of as much as 5% compared to exact solutions when tested with small examples.

Utilizing the aforementioned models, two transportation sustainability studies are then conducted for the adoption of Alternative Fuel Vehicles (AFVs). The challenges in adopting AFVs are directly related to the transportation infrastructure problems since the initial AFV refueling locations will need to provide comparable convenient travel experience for the early adopters when compared to the already matured gasoline fuel based transportation infrastructure. This work demonstrates the significance of the integration between travel demand model and infrastructure problems, but also draws insightful policy measurements regarding AFV adoption.

The first application study attempts to measure the household inconvenience level of operating AFVs. Two different scenarios are examined from two behavioral assumptions – keeping currently reported pattern and minimizing the inconvenience cost through HAPPR or HAPPC. From these patterns, the personal or household inconvenience level is derived as compared to the original pattern, providing quantified data on how the public sector would compensate for the increases in travel disutility to ultimately encourage the attractiveness of AFVs.

From the supply side of the AFV infrastructure, Location-HAPP is applied to the incubation of the minimum refueling infrastructure required to support early adoption of Hydrogen Fuel Cell Vehicles (HFCVs). One of the early adoption communities targeted by auto manufacturers is chosen as the study area, and then three different values of accessibility are tested and measured in terms of tolerances to added travel time. Under optimal conditions, refueling trips are found to be toured with other activities. More importantly, there is evidence xx that excluding such vehicle-infrastructure interactions as well as routing and scheduling interactions can result in over-estimation of minimum facility requirement.