Contributing to the recent interest in the dynamics of activity-travel patterns, this chapter discusses a framework of an agent-based modeling approach focusing on the dynamic formation of (location) choice sets. Individual travelers are represented as agents, each with their cognition of the environment, habits, and activity-travel patterns. Agents learn through their experiences with the transport systems, changes in the environments and from their social network. Conceptually, agents are assumed to have an aspiration level associated with choice sets that in combination with evaluation results determine whether the agent will start exploring or persist in habitual behavior; an activation level of each (location) alternative that determines whether or not the alternative is included in the choice set in the next time step, and an expected (utility) function to evaluate each (location) alternative given current beliefs. Each of these elements is dynamic. Based on principles of reinforcement learning, Bayesian learning, and social comparison theories, the framework specifies functions for experience-based learning, extended and integrated with social learning.
So-called activity-based models have rapidly gained interest in the transportation research community. These models predicts and simulate in a coherent fashion multiple facets of activity-travel behavior including which activities are conducted, when, where, for how long, with whom, and the transport mode involved. To the extent that these models have been actively implemented, some type of simulation is used to implement predicted activity-travel in time and space. The majority of these simulations are based on Monte Carlo simulations; others use agents such as Albatross (Arentze and Timmermans, 2001, 2003a, 2004a) and Aurora (e.g., Joh, et al. 2006). An overview of these developments is given in Timmermans, et al. (2002). In addition to these comprehensive models, agent-based simulations have also been suggested for particular facets of activity-travel choice (e.g., Charypar and Nagel, 2003; Balmer, et al. 2004; Rosetti and Liu, 2004; Hertkort and Wagner, 2005; Rindsfuser and Klügl, 2005).
Although the theoretical underpinnings of these agent-based models differ, they have in common the assumption that individuals will choose within their choice sets the alternative they prefer, sometimes subject to a set of constraints (Ben-Akiva and Boccara, 1995; Pellegrini, et al., 1997, Cascetta and Papola, 2001). In most of these models, however, the construction and composition of individual choice sets is not explicitly modeled. Choice sets are typically assumed given or derived on the basis of some arbitrary rule (Swait and Ben-Akiva, 1987; Thill and Horowitz, 1997; Swait, 2001). The delineation of choice sets is particularly important in large-scale micro-simulation systems, which are receiving increasing attention in activity-based travel-demand modeling and integrated land-use – transportation systems. As expected, knowing the choice set from which an alternative is selected significantly decreases the complexity and may improve the performance of these large-scale systems (Shocker, et al., 1991). In this context, the choice set refers to the set of discrete alternatives known by the individual, which is a subset of the universal choice set that consists of all alternatives available to the decision maker. Known means that the individual knows the attributes that are potentially relevant for evaluation under specific contextual conditions in the activity-travel decision-making process. Note that this definition differs from commonly used terminology in marketing, where a distinction is made between awareness set, evoked set, consideration set and choice set (Timmermans and Golledge, 1990). We can refine our framework along these lines, but that is beyond the goal of the present chapter.
As a part of the FEATHERS model (Arentze, et al., 2006; Janssens, et al., 2006), an extension and elaboration of Aurora (Joh et al., 2006), an agent-based system which incorporates different types of dynamics and learning discussed in Arentze and Timmermans (2003) is developed. This chapter discusses the conceptual framework that addresses one type of dynamic: the formation and dissolution of personal choice sets, which lays the foundation for the longer-term dynamics of the FEATHERS models. It should be stated from the outset that the discussion below mainly concerns location choice set, but the basic mechanism can be applied as building blocks for multiple facets of activity-travel patterns, including person choice set, mode choice set, and so on.