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71 Modeling Short-Term Dynamics in Activity-Travel Patterns From Aurora to Feathers Theo Arentze, TU Eindhoven, Netherlands Harry Timmermans, TU Eindhoven, Netherlands Davy Janssens, Hasselt University, Transportation Research Institute, Belgium Geert Wets, Hasselt University, Transportation Research Institute, Belgium Most operational models of activity-traveldemand, including nested logit models (e.g.,Vovsha et al. 2004), CEMDAP (Bhat et al. 2004), FAMOS (Pendyala et al. 2005) and Albatross (Arentze and Timmermans 2000, 2005a) have been devel- oped to predict activity-travel patterns. The main contri- bution of these models is to offer an alternative to the four-step models of travel demand, better focusing on the consistency of the submodels and proving increased sensi- tivity to a wider range of policy issues. These models are most valuable for predicting the impact of land use and transportation policies on typical activity-travel patterns, allowing policy makers to assess the likely impact of such policies in relation to changing travel demand and a set of accessibility, mobility, and environmental performance indicators. For short-term dynamics in activity-travel patterns, these activity-based models at their current state of devel- opment have much less to offer. For example, route choice and the aggregate impact of individual-level route choice decisions on activity generation and rescheduling behavior is not included in these models. Short-term dynamics are really not addressed at all, and issues such as uncertainty, learning, and nonstationary environ- ments are also not considered. Of course, there is a wide variety of traffic assignment, route, and departure choice models, but at their current state of development, it is fair to say that the behavioral contents of these models from an activity-based perspective are still relatively weak and that comprehensive dynamic models are still lacking. Especially in the context of day-to-day manage- ment of traffic flows, such activity-based models of short-term dynamics in activity-travel patterns would serve their purpose. To complement the Albatross system, the Urban Group therefore started the development of Aurora, a model focusing on the rescheduling of activity-travel pat- terns. The foundations of this model appear in Timmer- mans et al. (2001) and Joh et al. (2003, 2004), focusing on the formulation of a comprehensive theory and model of activity rescheduling and reprogramming decisions as a function of time pressure. Apart from duration adjust- ment processes, the Aurora model also incorporated other potential dynamics, such as change of destination, transport mode, and other facets of activity-travel pat- terns. Later, this model was extended to deal with uncer- tainty (Arentze and Timmermans 2004), various types of learning (Arentze and Timmermans 2005b, 2006), and responses to information provision (Arentze et al. 2005; Sun et al. 2005). Finally, a framework to implement this model as a multiagent simulation system has been devel- oped and explored (Arentze et al. 2005). In 2005, a research program coordinated by IMOB (Transporta- tion Research Institute) was funded by IWT (Institute for the Promotion of Innovation by Science and Technology in Flanders), Belgium. The goal of this program, in addi- tion to exploring the potential use of new technology on collecting travel data, is to develop a prototype, activity- based model of transport demand for Flanders, Belgium. The basis of this model, which has been given the acronym Feathers, will be the extended version of Aurora, complemented with some additional concepts. This paper reports the current development of this agent-based microsimulator that allows one to simulate
