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A multi-agent approach to cooperative traffic management and route guidance

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Listed:
  • Adler, Jeffrey L.
  • Satapathy, Goutam
  • Manikonda, Vikram
  • Bowles, Betty
  • Blue, Victor J.

Abstract

This paper explores the use of cooperative, distributed multi-agent systems to improve dynamic routing and traffic management. On the supply-side, real-time control over the transportation network is accomplished through an agent-based distributed hierarchy of system operators. Allocation of network capacity and distribution of traffic advisories are performed by agents that act on behalf of information service providers. Driver needs and preferences are represented by agents embedded in intelligent in-vehicle route guidance systems. Negotiation between ISP and driver agents seek a more efficient route allocation across time and space. Results from simulation experiments suggest that negotiation can achieve more optimal network performance and increased driver satisfaction.

Suggested Citation

  • Adler, Jeffrey L. & Satapathy, Goutam & Manikonda, Vikram & Bowles, Betty & Blue, Victor J., 2005. "A multi-agent approach to cooperative traffic management and route guidance," Transportation Research Part B: Methodological, Elsevier, vol. 39(4), pages 297-318, May.
    • Handle: RePEc:eee:transb:v:39:y:2005:i:4:p:297-318
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    References listed on IDEAS

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    1. Adler, Jeffrey L. & Cetin, Mecit, 2001. "A direct redistribution model of congestion pricing," Transportation Research Part B: Methodological, Elsevier, vol. 35(5), pages 447-460, June.
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    Cited by:

    1. Prajakta Desai & Seng W Loke & Aniruddha Desai, 2017. "Cooperative vehicles for robust traffic congestion reduction: An analysis based on algorithmic, environmental and agent behavioral factors," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-19, August.
    2. Xia Xiao & Xiaowu Mu, 2017. "Consensus of linear multi-agent systems with communication delays by using the information of second-order neighbours under intermittent communication topology," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(1), pages 200-208, January.
    3. Yangjie Chen & Fan Zhang & Jianning Li, 2022. "Anti-Disturbance Fault-Tolerant Constrained Consensus for Time-Delay Faulty Multi-Agent Systems with Semi-Markov Switching Topology," Mathematics, MDPI, vol. 10(23), pages 1-17, December.
    4. Liu, Ronghui & Van Vliet, Dirck & Watling, David, 2006. "Microsimulation models incorporating both demand and supply dynamics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(2), pages 125-150, February.
    5. Emiliano Brancaccio & Mauro Gallegati & Raffaele Giammetti, 2022. "Neoclassical influences in agent‐based literature: A systematic review," Journal of Economic Surveys, Wiley Blackwell, vol. 36(2), pages 350-385, April.
    6. Kshama Dwarakanath & Svitlana Vyetrenko & Tucker Balch, 2024. "Empirical Equilibria in Agent-based Economic systems with Learning agents," Papers 2408.12038, arXiv.org.
    7. Pi, Xidong & Qian, Zhen (Sean), 2017. "A stochastic optimal control approach for real-time traffic routing considering demand uncertainties and travelers’ choice heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 710-732.
    8. Ge, Jiaqi & Polhill, J. Gareth & Craig, Tony P., 2018. "Too much of a good thing? Using a spatial agent-based model to evaluate “unconventional” workplace sharing programmes," Journal of Transport Geography, Elsevier, vol. 69(C), pages 83-97.
    9. Kshama Dwarakanath & Svitlana Vyetrenko & Peyman Tavallali & Tucker Balch, 2024. "ABIDES-Economist: Agent-Based Simulation of Economic Systems with Learning Agents," Papers 2402.09563, arXiv.org.

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