Application of an activity-based travel-demand model incorporating a rule-based algorithm

In this paper an activity-based travel-demand model called AMOS is described. The model system is capable of simulating changes in individual activity and travel behavior that may be brought about by a change in the transportation system. These simulations may then be used to predict the impacts of various transportation policies on regionwide travel characteristics. A rule-based activityscheduling algorithm is at the heart of AMOS. The algorithm simulates changes in activity and travel patterns while recognizing the presence of constraints under which travelers make decisions. Operationally, the algorithm reads the baseline activity and travel pattern of an individual and then determines the most probable adjustments that the individual may make in response to a transportation policy. In this paper, the scheduling algorithm is described in detail and sample results from a case study in the Washington, DC metropolitan area are provided.