A stochastic activity-based model and a strategic agent framework for traffic simulation
Seminar of Johan Barthelemy, Associate Research Fellow at SMART Infrastructure Facility (University of Wollongong)
On Wednesday, September 14, 2016 at 1PM (Etilux room)
The work presented in this seminar focuses on the simulation of the mobility behaviour of (large) populations, i.e. the travel demand and its assignment on the road network.
The travel demand is simulated using a stochastic activity-based approach in which the travel demand is derived from the activities performed by the individuals. The proposed model is distribution-based and requires only minimal information, but is designed to easily take advantage of any additional network-related data available. The proposed activity-based approach has been applied to the Belgian synthetic population. The quality of the agent behaviour is discussed using statistical criteria and results shows that the model produces satisfactory results.
The demand can then be assigned on the network. For that purpose, a novel agent-based framework is being developed. The proposal relies on the assumption that travellers take routing policies rather than paths, leading us to introduce the possibility for each simulated agent to apply, in real time, a strategy allowing him to possibly re-route his path depending on the perceived local traffic conditions. The re-routing process allows the agents to directly react to any change in the road network. For the sake of simplicity, the strategy is modelled with a simple neural network whose parameters are determined during a preliminary training stage. The inputs of such neural network read the local information about the route network and the output gives the action to undertake: stay on the same path or modify it. As the agents use only local information, the overall network topology does not really matter, thus the strategy is able to cope with large networks. Numerical experiments are performed to test the robustness and adaptability to new environments. The methodology is also compared against MATSim and real world data. The outcome of the experiments suggest that this work-in-progress already produces encouraging results.