Hybrid agent based simulation with adaptive learning of travel mode choices for University commuters (WIP)
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.
Nagesh Shukla, Albert Munoz, Jun Ma, and Nam Huynh. "Hybrid Agent based Simulation with Adaptive Learning of Travel Mode Choices for University Commuters (WIP)" Workshop on model-driven approaches for simulation engineering (Mod4Sim) Symposium on Theory of Modeling and Simulation, part of the SCS SpringSim 2013 conference, April 7-10, 2013, San Diego, CA (USA) (2013).