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Hybrid Agent Based Simulation With Adaptive Learning Of Travel Mode Choices For University Commuters (Wip), Nagesh Shukla, Albert Munoz, Jun Ma, Nam Huynh

Albert Munoz

This paper presents a methodology for developing a hybrid agent-based micro-simulation model to capture the impacts of commuter travel mode choices on a University campus transport network. The proposed methodology involves: (i) developing realistic population of commuter agents (students and staff); (ii) assigning activity lists and travel mode choices to agents using machine learning method; and, (iii) traffic micro-simulation of the study area transport network. This furthers the understanding of current transport modal distributions, factors affecting the travel mode choice decisions, and, network performance through a number of hypothetical travel scenarios.