Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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    Choice Modeling Perspectives on Social Networks, Social Influence, and Social Capital in Activity and Travel Behavior
    (2015) Maness, Michael; Cirillo, Cinzia; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Understanding the determinants of activities and travel is critical for transportation policymakers, planners, and engineers to design and manage transportation systems. These systems, and their externalities, are interwoven with social systems in communities, cities, regions, and societies. But discrete choice models - the predominant modeling tool for researching travel behavior and planning transportation systems - are grounded in theories of individual decision-making. This dissertation expands knowledge about the incorporation of social interactions into activity-travel choice models in the areas of social capital and social network indicators; social influence motivations and informational conformity; and misspecification errors from social network data collection. Incorporating social capital into activity choice models involves using social capital indicators from surveys. Using a position generator question type, the role of social network occupational diversity in activity participation was explored and the performance of models using name generator and position generator data was compared. Access to the resources embedded in diverse networks (extensity) was found to positively correlate with leisure activity participation. Compared to core network indicators from name generators, position generator indicators were typically better at predicting activity participation in a cross-validation study. Current models of social influence in travel do not account for varying motivations for social influence such as for accuracy, affiliation, and self-concept. To test for an accuracy motivation, a latent class discrete choice model was formulated that places individuals into classes based on information exposure. Contrasting with existing work, this model showed that "more informed" households are more likely to own bicycles due to preference changes causing less sensitivity to smaller home footprints and limited incomes. A Bayesian prediction procedure was used to derive distributions of local-level equilibria and social influence elasticity. The effect of errors in social network data collection using name and position generators is not fully understood for choice models. In a case study, the social network occupational diversity measure was robust to varying position generator lengths. Simulation experiments tested the implications of social network structure, misspecification, and small samples on social influence choice models where sample size, social influence strength, and degree of misspecification had the greatest impact.
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    Modeling Vehicle Ownership Decisions in Maryland: A Preliminary Stated Preference Survey and Model
    (2010) Maness, Michael; Cirillo, Cinzia; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In the near future, the culmination of new vehicle technologies, greater competition in the energy markets, and government policies to fight pollution and reduce energy consumption will result in changes in the United States' vehicle marketplace. This project proposes to create a stated preference (SP) survey along with discrete choice models to predict future demand for electric, hybrid, alternative fuel, and gasoline vehicles. The survey is divided into three parts: socioeconomics, revealed preference (RP), and SP sections. The socioeconomics portion asks respondents about themselves and their households. The RP portion asks about household's current vehicles. The SP section presents respondents with various hypothetical scenarios over a future five-year period using one of three game designs. The designs correspond to: changing vehicle technology, fuel pricing and availability, and taxation policy. With these changes to the vehicle marketplace, respondents are asked whether they will keep or replace their current vehicles and if he will purchase a new vehicle and its type. To facilitate the design and administering of the survey, a web survey framework, JULIE, was created specifically for creating stated preference surveys. A preliminary trial of the survey was conducted in September and October 2010 with a sample size of 141 respondents. Using the SP results from this preliminary trial, a multinomial logit model is used to estimate future vehicle ownership by vehicle type. The models show that the survey design allows for estimation of important parameters in vehicle choice.