Modeling residential self-selection in activity-travel behavior models : integrated models of multidimensional choice processes
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The focus of transportation planning, until the past three decades or so, was to provide adequate transportation infrastructure supply to meet the mobility needs of the population. Over the past three decades, however, in view of increasing suburban sprawl and auto dependence, the focus of transportation planning has expanded to include the objective of sustainable development. Contemporary efforts toward sustainability include, for example, integrated land-use and transportation planning, travel demand management, congestion pricing, and transit and non-motorized travel oriented development. Consequently, in an effort to understand individuals’ behavioral responses to (and to assess the effectiveness of) these policies, the travel demand modeling field evolved along three distinct directions: (a) Activity-based travel demand modeling, (b) Built environment and travel behavior modeling, and (c) Integrated land-use -- transportation modeling. The three fields of research, however, have progressed in a rather disjoint fashion. The overarching goal of this dissertation is to contribute toward the research needs that are at the intersection of the three fields of research identified above, and to bring the three research areas together into a unified research stream. This is achieved by the simultaneous consideration of the following three aspects, each of which is of high importance in each direction of research identified above: (1) The activity-based and tour-based approaches to travel behavior analysis, (2) Residential self-selection effects, and (3) Integrated modeling of long-term land-use related choices and medium- and short-term travel-related choices. To this end, a series of integrated models of multidimensional choice processes are formulated to jointly analyze long-term residential location decisions and medium- and short-term activity-travel decisions (such as auto ownership, bicycle ownership, commute mode choice, and daily time-use). The models are estimated and applied using data from the 2000 San Francisco Bay Area Travel Survey to understand and disentangle the multitude of relationships between long-, medium-, and short-term choices. This dissertation also formulates a multiple discrete-continuous nested extreme value model that can accommodate inter-alternative correlations and flexible substitution patterns across mutually exclusive subsets (or nests) of alternatives in multiple discrete-continuous choice models.