This paper develops a conceptual and econometric framework of non-work activity location choice that is comprehensive in its incorporation of spatial cognition, heterogeneity in preference behavior, and spatial interaction. The proposed framework subsumes a variety of restricted models including the multinomial logit, first-order state dependence logit, spatially correlated logit and mixed spatially correlated logit models. The applicability of the framework is demonstrated through an empirical analysis using the German Mobidrive data. The activity-based approach to travel analysis emphasizes the modeling of the activity-travel patterns of individuals, which may be characterized by six broad attributes: (a) Motivation or, equivalently, the purpose of each activity episode (such as work and shopping), (b) Location of participation of each activity episode (such as the work place or grocery store), (c) Sequencing of activity episodes and the time of day of each episode participation, (d) Mode used to travel to the episode location (for example, auto or transit), and (e) Solo or joint activity episode participation. Of these activity-travel attributes, the location of participation spatially pegs the daily activitytravel patterns of individuals. Thus, it is important to accommodate behavioral realism in models of activity location choice to produce accurate predictions of travel demand under changing landuse, demographic, and transportation system contexts. Moreover, an understanding of the factors that influence the choice of location can contribute to more effective land-use and zoning policies. For instance, a habit-persistent individual may be more likely to continue shopping at the same grocery store, rather than switching stores, in response to a new land-use policy that brings more shopping opportunities closer to home.
KeywordsThe choice of location of episode participation, and the factors that influence this choice, vary with activity purpose. Generally, the work location for most people is fixed in the shortterm (except for teleworking individuals). Non-work activity participation, on the other hand, is typically (though not always) characterized by a higher degree of spatial flexibility. In particular, the choice of location for non-work activities can vary not only across individuals but also across choice occasions of an individual. Thus, non-work location modeling is a challenging problem. At the same time, non-work location modeling is of interest not only from a transportation and urban planning perspective, but also from the perspective of service, retail and real estate businesses. For instance, predictions of where people shop, and spend their recreational and leisure time, plays an important role in the location and marketing decisions of businesses and firms [see, for example, (1), (2), (3), (4), and (5)].