IDEAS home Printed from https://ideas.repec.org/a/eee/transb/v39y2005i4p297-318.html
   My bibliography  Save this article

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

Author

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0191-2615(04)00051-7
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    7. 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.
    8. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Holgui­n-Veras, Jose & Cetin, Mecit & Xia, Shuwen, 2006. "A comparative analysis of US toll policy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(10), pages 852-871, December.
    2. Yang Liu & Yu (Marco) Nie, 2017. "A Credit-Based Congestion Management Scheme in General Two-Mode Networks with Multiclass Users," Networks and Spatial Economics, Springer, vol. 17(3), pages 681-711, September.
    3. Naroditskiy, Victor & Steinberg, Richard, 2015. "Maximizing social welfare in congestion games via redistribution," LSE Research Online Documents on Economics 62771, London School of Economics and Political Science, LSE Library.
    4. Cipriani, Ernesto & Mannini, Livia & Montemarani, Barbara & Nigro, Marialisa & Petrelli, Marco, 2019. "Congestion pricing policies: Design and assessment for the city of Rome, Italy," Transport Policy, Elsevier, vol. 80(C), pages 127-135.
    5. Zheng, Yu & Zhang, Xiaoning & Liang, Zhe, 2020. "Multimodal subsidy design for network capacity flexibility optimization," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 16-35.
    6. Ren-Yong Guo & Hai-Jun Huang & Hai Yang, 2019. "Tradable Credit Scheme for Control of Evolutionary Traffic Flows to System Optimum: Model and its Convergence," Networks and Spatial Economics, Springer, vol. 19(3), pages 833-868, September.
    7. François Mirabel & Mathias Reymond, 2011. "The urban toll revenue recycling : what is the optimal share distributed towards mass transit system?," Working Papers hal-01830227, HAL.
    8. Li, Zhi-Chun & Huang, Hai-Jun & Yang, Hai, 2020. "Fifty years of the bottleneck model: A bibliometric review and future research directions," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 311-342.
    9. Chen, Ruoyun & Nozick, Linda, 2016. "Integrating congestion pricing and transit investment planning," Transportation Research Part A: Policy and Practice, Elsevier, vol. 89(C), pages 124-139.
    10. Yang Liu & Xiaolei Guo & Hai Yang, 2009. "Pareto-improving and revenue-neutral congestion pricing schemes in two-mode traffic networks," Netnomics, Springer, vol. 10(1), pages 123-140, April.
    11. Guo, Xiaolei & Yang, Hai, 2010. "Pareto-improving congestion pricing and revenue refunding with multiple user classes," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 972-982, September.
    12. Naroditskiy, Victor & Steinberg, Richard, 2015. "Maximizing social welfare in congestion games via redistribution," Games and Economic Behavior, Elsevier, vol. 93(C), pages 24-41.
    13. Liu, Yang & Nie, Yu (Marco), 2011. "Morning commute problem considering route choice, user heterogeneity and alternative system optima," Transportation Research Part B: Methodological, Elsevier, vol. 45(4), pages 619-642.
    14. Perera, Loshaka & Thompson, Russell G. & Wu, Wenyan, 2021. "Toll and subsidy for freight vehicles on urban roads: A policy decision for City Logistics," Research in Transportation Economics, Elsevier, vol. 90(C).
    15. Zhi-Chun Li & Ya-Dong Wang & William Lam & Agachai Sumalee & Keechoo Choi, 2014. "Design of Sustainable Cordon Toll Pricing Schemes in a Monocentric City," Networks and Spatial Economics, Springer, vol. 14(2), pages 133-158, June.
    16. Nie, Yu (Marco) & Yin, Yafeng, 2013. "Managing rush hour travel choices with tradable credit scheme," Transportation Research Part B: Methodological, Elsevier, vol. 50(C), pages 1-19.
    17. Chen, Linxi & Yang, Hai, 2012. "Managing congestion and emissions in road networks with tolls and rebates," Transportation Research Part B: Methodological, Elsevier, vol. 46(8), pages 933-948.
    18. Holgun-Veras, Jos & Cetin, Mecit, 2009. "Optimal tolls for multi-class traffic: Analytical formulations and policy implications," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(4), pages 445-467, May.
    19. Tian, Li-Jun & Yang, Hai & Huang, Hai-Jun, 2013. "Tradable credit schemes for managing bottleneck congestion and modal split with heterogeneous users," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 54(C), pages 1-13.
    20. Nie, Yu (Marco) & Liu, Yang, 2010. "Existence of self-financing and Pareto-improving congestion pricing: Impact of value of time distribution," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(1), pages 39-51, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transb:v:39:y:2005:i:4:p:297-318. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/548/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.