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

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

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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. 1)
    2. 2)
      • Kaysi, I.: `Framework and models for provision of driver information system’', 1991, PhD, The Cambridge University.
    3. 3)
      • H.S. Mahmassani , G.L. Chang . Dynamic aspects of departure time choice behavior in a commuting system: theoretical framework and the experimental analysis. Transp. Res. Rec. , 88 - 101
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
      • Schleiffer, R.: `Traffic itself is simple-just analyzing it is not', Proc. 33rd Annual Hawaii Int. Conf. on System Sciences, January 2000, Hawaii, USA, p. 10–11.
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
      • Rao, A.S.: `AgentSpeak(L): BDI Agents speak out in a logical computable language', Proc. Seventh European Workshop on Modeling Autonomous Agents in a Multi-Agent World, January 1996, Eindhoven, The Netherlands, p. 42–55.
    16. 16)

Related content

This is a required field
Please enter a valid email address