Vehicle miles of travel (VMT) is a key indicator of travel demand in the United States. Since 1995 total VMT and VMT per capita has fluctuated, with notable declines in the late 2000s and accelerated increases in the last 7 years. Since 1995, the National Household Travel Survey (NHTS) has tracked the household share of total VMT to shed light on the demographic and behavioral data behind personal vehicle travel. The household share of VMT, while still a majority, has declined every NHTS year since at least 1995. Meanwhile, household VMT has stagnated around 2.25 trillion miles since the 2001 survey. With such unprecedented travel demand changes, the current transportation technology revolution, and the climate of uncertainty, it is critical to understand why household VMT is changing and how this might affect future roadway demand. This thesis examines demographic, socioeconomic and behavioral factors that influence VMT, including both factors with existing research and some untraditional factors, using new data and methodologies.

Master of Science / Department of Computer Science / Doina Caragea / The goal of the project is to create a database for storing the National Household Travel Survey (NHTS) data, and a user interface to query the database. Currently, the survey data is stored in excel files in the CSV format, which makes it hard to perform complex analyses over the data. Analyses of interest to transportation community include comparisons of the trips made by urban household to those made by rural household, finding the average trip time spent based on ethnicity, the total travel time of a particular household, the preferred vehicle by a specific household, average time spent per shopping trip, etc. The tool designed for the purpose of querying the NHTS database is a Python-based Web application. Django is used as the Web framework for this project and PostgreSQL is used for the back-end purpose. The user interface consists of various drop-down lists, text-boxes, buttons and other user interface components that facilitate querying the database and presenting the results in formats that allow easy interpretation. FusionCharts Django-Wrapper and FusionCharts Jquery-Plugin are used to visualize the data in the chart form. A Codebook of the NHTS dataset is also linked for the reference purpose at any point for the user. The tool built in the project allows the user to get a deeper understanding of the data, not only by plotting the data in the form of line charts, bar charts, two column graph, but also by providing the results of the queries in the CSV format for further analysis.

Spatial transferability of travel forecasting models, or the ability to transfer models from one geographical region to another, can potentially help in significant cost and time savings for regions that cannot invest in extensive data-collection and model-development procedures. This issue is particularly important in the context of tour-based/activity-based models whose development typically involves significant data inputs, skilled staff, and long production times. However, most literature on model transferability has been in the context of traditionally used trip-based models, particularly for linear regression-based trip generation and logit-based mode choice models, with little evidence on the transferability of activity-based models and that of emerging model structures. The overarching goal of this dissertation is to assess the spatial transferability of activity-based travel demand models. To this end, the specific objectives are to: 1. Survey the literature to synthesize: (a) the approaches used to transfer models, (b) the metrics used to assess model transferability, (c) the available evidence on spatial transferability of travel models, and (d) notable gaps in literature; 2. Lay out a framework for assessing the spatial transferability of activity-based travel forecasting model systems, and evaluate alternative methods/metrics used for assessing the transferability of specific model components and their parameters; 3. Conduct empirical assessments of spatial transferability of the following two model components used in today's activity-based model systems: (a) daily activity participation and time-use models, and (b) tour-based time-of-day choice models. Data from the 2009 National Household Travel Survey (NHTS) and the 2000 San Francisco Bay Area Travel Survey (BATS) were used for these empirical assessments; 4. Conduct empirical assessments of model transferability using emerging model structures that have begun to be used in activity-based model systems - specifically the multiple discrete-continuous extreme value (MDCEV) model; 5. Investigate alternate ways of enhancing model transferability; specifically: (a) pooling data from different geographical regions, and (b) improvements to the model structure. The dissertation provides a framework for assessing the transferability of activity-based models systems, along with empirical evidence on the pros and cons of alternative methods and metrics of transferability assessment. The results suggest the need to consider model sensitivity to changes in explanatory variables as opposed to relying solely on the ability to predict aggregate distributions. Updating the constants of a transferred model using local data (a widely used method to transfer models) was found to help in increasing the model's ability to predict aggregate patterns but not necessarily in enhancing its sensitivity to changes in explanatory variables. Also, transferability assessments ought to consider sampling variance in parameter estimates as opposed to only the point estimates. Empirical analysis with the daily activity participation and time-use model shed new light on the prediction properties of the MDCEV model structure that have implications for model transferability. This led to the development of a new model structure called the multiple discrete continuous heteroscedastic extreme value (MDCHEV) model that incorporates heteroscedasticity in the model's stochastic distributions and helps in enhancing model transferability. Transferability assessment of the time-of-day choice models show encouraging evidence of transferability of a large proportion of the model coefficients, albeit except important parameters such as the travel time coefficients. Collectively, there is evidence that pooling data from multiple regions may help in building better transferable models than those transferred from a single region.

Over the past several decades, the senior age group has become the fastest growing segment of the population in the United States (Warner, 2011). This study seeks to contribute to the ongoing discussion of the impacts that the increases in senior travel will have on the future transportation systems and planning efforts. The main objective of this research is to conduct an explorative analysis of the changes in senior travel behavior over the past two decades and discuss the implications of these changes to transportation planning in the future. This thesis seeks to further understanding of this topic by providing a detailed analysis and consideration of relevant contexts through a review of previous studies and the author's background in the field of transportation. Results indicate significant changes in travel behaviors and make-up of the senior population. Over the three (1990, 2001, and 2009) survey periods, senior travel changed as a result of increased activity and a need to maintain their typical way of life well into older age. As the baby boom generation continues to out travel each previous generation, there is no evidence to assume that as they reach retirement age that trend will end. Seniors today are remaining active and working well into their older age and the age group has continued to increasingly contribute to total travel. These increases will be echoed by the baby boom generation and must be considered by traffic forecasters, researcher and policy makers in the future.

Travel demand analysis has traditionally focused on exploring and modeling travel behavior on weekdays. This emphasis on weekday travel behavior analysis was largely motivated by the presence of well-defined peak periods, primarily associated with the journey to and from work. Most travel demand models are based on weekday travel characteristics and purport to estimate traffic volumes for daily or peak weekday conditions. Much of the planning and policy making that occurs in transportation arena in response to weekday travel behavior and forecasts. More recently, there had been a growing interest in exploring, understanding, and quantifying weekend travel characteristics. The ability to do this has been limited due to the non-availability of travel survey data that includes weekend trip information. Most travel surveys collect information about weekday travel behavior and ignore weekend days. However, the 2001 National Household Travel Survey includes a substantial sample that provided detailed trip information for weekend days and therefore this dataset offers a key opportunity to explore in-depth weekend travel characteristics. Weekend travel behavior is expected to be substantially different from the weekday travel behavior for difference in several spatial and temporal constraints. The difference in constraints can also lead to a change in trip chaining patterns on weekdays and weekends. Differences in constraints coupled with socio-economic changes characterized by greater disposable income, time-constrained lives, and greater discretionary activity opportunities point towards the growing role that weekend travel behavior is going to play in transportation planning and policy-making. This thesis provides a comprehensive analysis of weekend travel behavior using the 2001 NHTS. Differences and similarities between weekday and weekend travel behavior are identified and presented for different urban areas sizes varying according to Metropolitan Statistical Area (MSA) size. Models of weekend and weekday travel behavior are developed to capture the structural relationship of socio-demographics, activity durations, and travel duration are developed using structural equations modeling approaches to better understand the relationships among these aspects of travel behavior on weekdays and weekends. This report is supposed to act as an updated data guide to the National Cooperative Highway Research Program's (NCHRP) Report 365 titled "Travel Estimation Techniques for Urban Planning" aims at studying the changes in behavioral characteristics between two categories of the day of week - a weekday and a weekend based on personal, household and trip characteristics.

The number of elderly is increasing; to meet their transportation needs, it is important to clearly understand their travel patterns and preferences. Since travel patterns and preferences depend on socio-demographic and other factors, it is essential to identify these factors first to understand the travel behavior of the elderly. The main purpose of this thesis is to analyze the travel patterns and preferences of the elderly age 65 and above using 2009 National Household Travel Survey (NHTS) data. This thesis presents a detailed descriptive analysis of 2009 NHTS data to understand the travel patterns of the elderly. Along with a descriptive analysis, a multinomial logit model and a mixed- multinomial logit model are estimated to explore the factors associated with the overall travel preferences of the elderly and to identify individuals among the elderly who are the least mobile and at risk for social isolation. The analysis results indicate the differences in the trip characteristics between the elderly and non-elderly. Variation is found even among the different groups of the elderly. The model estimation results show the presence of different travel preferences among the elderly and identify those individuals among the elderly who are immobile for longer periods (e.g., a week) and at risk for social isolation. Elderly individuals with different travel preferences should be considered separately in research to determine the appropriate outcomes that can help transportation planners and policy makers improve planning and policy related to elderly individuals.

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