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Recent progress in activity-based analysis has witnessed the development of some dynamic models of activity-travel rescheduling decisions. Most of this work involved descriptive analyses. Timmermans et al. (2001) elaborated this work and developed a more comprehensive theory and model of activity rescheduling and re-programming decisions as a function of time pressure. They assumed that rescheduling decisions are based on the marginal utility of time, subject to constraints. However, they also assumed bounded rationality due to the incomplete information that brings about uncertainty and imperfect behavior. They showed that various types of behavior could be embedded within an overall theory of rescheduling decisions. Originally, their theory and simulation model was only concerned with duration adjustment processes, and did not incorporate other potential dynamics such as change of destination, transport mode, and other facets of activity-travel patterns. Later, multiple choice facets were included, leading to their Aurora model (Joh, Arentze and Timmermans, 2002, 2003). Work to date on this model has focused on problems such as solving the problem of model estimation (Joh, et al., 2003), and estimating the duration parameters on cross-sectional empirical data (Joh, et al., 2004). However, if the model is going to be used for predicting short-term adjustment and traffic flows, an implementation of the model is required and additional operational decisions are required. This paper addresses this problem. It reports on the development of an agent-based micro-simulator that allows one to simulate activity-travel rescheduling decision in high resolution space and time. The implementation is illustrated using activity-travel diary data that for the Eindhoven region was collected originally to better understand the choice of urban parks in the study area.
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Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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