Agent-based approach to model commuter behaviour's day-to-day dynamics under pre-trip information

This study reports on the tentative use of a multi-agent micro-simulation framework to address the issue of assessing commuter behaviour's day-to-day dynamics under pre-trip information. A Bayesian updating model is adopted to capture the reasoning mechanism by which commuters update their travel time perceptions from one day to the next in light of information and their previous experience. The population of commuters is represented as a community of autonomous agents, and travel demand results from the decision-making deliberation performed by each individual of the population as regards route and departure time. The reasoning mechanism of commuters is modelled by means of a Belief, Desire and Intention architecture, which has been a central theme in the multi-agent systems literature since the early 1990s. Each part of this architecture is specified by a multi-agent programming language named as AgentSpeak (L). A simple simulation scenario was devised using a combination of Jason (a multi-agent simulator) and Paramics (a traffic simulator). The simulation results show that the overall performance of the system is very likely affected by exogenous information and personal travel experiences; also, accurate information can greatly affect driver's switching activities and improve daily commuting conditions. Moreover, the combination of micro-simulation and agent-based modelling technique shows a great potential to represent more realistic and more complex driver's behaviour under intelligent transport system environment.

[1]  Hani S. Mahmassani,et al.  Individual Decisions and Collective Effects in a Simulated Traffic System , 1986, Transp. Sci..

[2]  Ralf Schleiffer.  Intelligent agents in traffic and transportation , 2002 .

[3]  Hani S. Mahmassani,et al.  On Boundedly Rational User Equilibrium in Transportation Systems , 1987, Transp. Sci..

[4]  Hani S. Mahmassani,et al.  Dynamic models of commuter behavior: Experimental investigation and application to the analysis of planned traffic disruptions , 1990 .

[5]  Samer Madanat,et al.  Perception updating and day-to-day travel choice dynamics in traffic networks with information provision , 1998 .

[6]  Haris N. Koutsopoulos,et al.  Simulation Laboratory for Evaluating Dynamic Traffic Management Systems , 1997 .

[7]  Hani S. Mahmassani,et al.  Day-to-day evolution of network flows under real-time information and reactive signal control , 1997 .

[8]  André de Palma,et al.  Dynamic Model of Peak Period Traffic Congestion with Elastic Arrival Rates , 1986, Transp. Sci..

[9]  Rosaldo J. F. Rossetti,et al.  Using BDI agents to improve driver modelling in a commuter scenario , 2002 .

[10]  Ralf Schleiffer,et al.  Traffic itself is simple-just analyzing it is not , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[11]  Ana L. C. Bazzan,et al.  The impact of real-time information in a two-route scenario using agent-based simulation , 2002 .

[12]  Ronald van Katwijk,et al.  COORDINATION OF TRAFFIC MANAGEMENT INSTRUMENTS USING AGENT TECHNOLOGY , 2002 .

[13]  Sascha Ossowski,et al.  MULTIAGENT ARCHITECTURES FOR INTELLIGENT TRAFFIC MANAGEMENT SYSTEMS , 2002 .