The objective of this project is to develop and calibrate a computational process model of spatial learning, for the micro-simulation of travel dynamics where individual travelers’ decision-making is simulated. Travel decisions are usually made in large spatial environment, and therefore spatial knowledge is an important moderator in the decision-making process. Priori research in environmental psychology, geography and artificial intelligence has shown that spatial knowledge is usually incomplete, distorted, and idiosyncratic depending on personal experience. The assumption of complete and precise spatial knowledge in all current travel micro-simulation models is thus problematic. This project aims at closing the gap between theory and practice by enhancing a computational process model of spatial learning using tracking data over a multi-month period. The model, once incorporated in an overall travel micro-simulation framework, can potentially improve the realism and policy sensitivity of the simulation.
The objective of this research was to study travelers' route choice behavior in uncertain traffic networks with real-time information. The research wa...
The objective of this project is to develop an efficient algorithm and its computer implementation for the optimal adaptive routing problem that is pr...
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