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