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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.