Transportation is closely tied to choices: those that have to do with location - where to live, work and play; temporal choices - when to engage in certain activities; the choice of mode - how to get to those activities and so on. The choice of home and work locations is especially important since these are relatively long term fixtures and can significantly influence everyday travel decisions. Often a person's day starts at home and ends at home. For many workers, other daily activities are constrained by the time they spend at their employment locations. Decisions related to home-work travel can also influence other choices such as short term activity locations. Understanding the link between home and work is therefore an important part of policy making to manage or accommodate travel demand.
Traditionally, the approach taken by transportation professionals to match home and work locations has been to use trip distribution models, the most commonly used of which has been the gravity model. These models, which use aggregate zonal variables to match home and work, are still widely used by many planning organizations. This framework, while very useful in predicting aggregate trip distribution, overlooks much of what happens as the connection between people's home and work are established.
This dissertation poses the link between home and work as an outcome of a search process for employment, where both searchers (workers) and employers try to match one another through advertising, search, screening, offers and decision making. It proposes a framework for matching home and work at a disaggregate level that follows the job search process. Empirical sections pay close attention to search methods as these can inform the geographic scope of opportunities searchers know of. Distinctions between different search methods and the related commute outcomes are illustrated using data collected for this study. The role of contacts in general, and neighborhood level contacts in particular, in matching home and work is also investigated using different data sources. An agent based model of job-worker matching based on the proposed framework is also developed and tested using data from Minnesota.
While the overall emphasis is on a disaggregate approach and moves away from geographic (zonal) aggregation of decision makers, the study of contacts and their role serves to illustrate that travel and destination decisions are not independent of those around us. Focusing on the individual decision maker and following the process of job-worker matching can allow for models that are much more sensitive to changes in policy variables as they can accommodate the variability of tastes and responses among decision makers. On the other hand, the consideration of contacts, including those that may be neighbors, leaves the door open for consideration of behavior that may arise from interactions with others.