UC Irvine

Complex travel behavior places travel in a broader context than in the conventional trip-based approach. The activity-based approach provides an analysis framework that positions travel decisions as dependent on a collection of activities that form an agenda for participation and, therefore, cannot be properly analyzed on individual trip basis. The basic units of analysis for activity-based approaches are tours, which can be defined as sequences of trips and activities that begin and end at the same location. In this dissertation, I apply a tour-based approach to analyze complex travel behavior from three perspectives: sustainability, technology, and economics.

First, I examine the complex travel behavior of workers, who utilize a sustainable transport option, namely public transit. I identify dominant patterns of work tours and analyze factors affecting tour choice using Structural Equation Modeling (SEM). The results obtained by using the 2017 National Household Travel Survey dataset suggest that 80 percent of work tours consist of seven dominant tours and that tour choice is influenced by a set of socio-demographic, built environment, and activity-travel characteristics. Second, the complex travel behavior of people who use technology-enabled ride-hailing services, such as Uber/Lyft, is explored. In particular, I identify heterogeneous groups of ride-hailing users by using Latent Class Analysis, analyze the activity-travel patterns of each of these groups, and discuss the ramifications of that behavior to policy directives.

Lastly, I explore the travel behavior of workers, again in terms of tours, when they are exposed to an economic downturn, the 2007-2009 recession. I apply multi-group SEM to analyze changes in tour choice during the recession (2009) compared to pre- (2006) and post-recession (2012) years. Using American Time Use Survey data, this study shows that activity-travel relationships and their role in tour choices differed significantly in the recession year. The results of this study provide insights into potential changes in worker’s travel demand during a recession, which would contribute to building better pattern choice sets in tour-based models.

The common thread throughout this dissertation is the development of a framework for analyzing complex travel behavior under disruptive changes due to environment, technology, and economics forces.