WITS 2020 pp 157-166 | Cite as

Intersection Management Approach based on Multi-agent System

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 745)


For several decades, urban congestion causes various problems such us pollution, road wares, and congestion in intersections which deteriorates the quality of life of citizens who live in big cities. Different methods proposed to reduce urban congestion, notably traffic regulation that attend tremendous attention recently. In past years, the usage of tools from artificial intelligence, particularly distributed methods and multi-agent systems, which allow to design new methods for traffic regulation. In this context, a Multi-Agent approach for intersection management system based on the principle of trajectory reservation has been proposed to reduce the travel time average and air pollution.


Intersection management Connected vehicle Multi-agent system IAS ITS 


  1. 1.
    Barth M, Boriboonsomsin K (2008) RealWorld carbon dioxide impacts of traffic congestion. In: Transportation research record, pp 163–171Google Scholar
  2. 2.
    Zhang K, Batternan S (2013) Air pollution and health risks due to vehicle traffic. In: Science of the total environment, pp 307–316Google Scholar
  3. 3.
    Hennessy DA, Wiesenthal DL, Kohn PM (2000) The influence of traffic congestion, daily hassles, and trait stress susceptibility on state driver stress: an interactive perspective. J Appl Biobehav Res 5(2):162–179Google Scholar
  4. 4.
    Hao P, Wang C, Wu G, Boriboonsomsin K, Barth M (2017) Evaluating the environmental impact of traffic congestion based on sparse mobile crowd-sourced data. In: IEEE conference on technologies for sustainability (SusTech), Phoenix, AZ, pp 1–6.
  5. 5.
    Stone P, Veloso M (2000) Multiagent systems: a survey from a machine learning perspective. Autonom Rob 8(3):345–383CrossRefGoogle Scholar
  6. 6.
    Dresner K, Stone P (2008) A multiagent approach to autonomous intersection management. J Artif Intel Res 31:591–656Google Scholar
  7. 7.
    Robertson DI TRANSYT: a traffic network study tool. RRL report, Road Research LaboratoryGoogle Scholar
  8. 8.
    Hunt PB, Robertson DI, Bretherton RD, Royle MC (1982) The SCOOT on-line traffic signal optimisation technique. In: Traffic engineering and control, vol 23, issue 4, pp 190–192Google Scholar
  9. 9.
    Sims AG, Dobinson KW (1980) The Sydney coordinated adaptive traffic (SCAT) system philosophy and benefits. IEEE Trans Veh Technol 29(2):130–137CrossRefGoogle Scholar
  10. 10.
    Lowrie PR (1982) The Sydney coordinated adaptive trafic system-principles, methodology, algorithms. In: International conference on road traffic signalling, London, United KingdomGoogle Scholar
  11. 11.
    Henry JJ, Farges JJ, Tuffal J (1983) The PRODYN real time traffic algorithm. In: Proceedings of the international federation of automatic control (IFAC) conference, IFAC, Baden-BadenGoogle Scholar
  12. 12.
    Zhao D, Dai Y, Zhang Z (2012) Computational intelligence in urban traffic signal control: a survey. IEEE Trans Syst Man Cybern Part C: Appl Rev 42(4):485–494CrossRefGoogle Scholar
  13. 13.
    Zhao L, Li Li, Li Z (2011) A fast signal timing algorithm for individual oversaturated intersections. IEEE Trans Intell Transp Syst 12(1):280–286MathSciNetCrossRefGoogle Scholar
  14. 14.
    Cajias R, Pardo A-G, Camacho D (2011) A multiagent simulation platform applied to the study of urban traffic lights. In: The 6th international conference on software and data technologies (ICSOFT 2011), pp 154–159Google Scholar
  15. 15.
    Zou X, Levinson D (2003) Vehicle-based intersection management with intelligent agents. In: ITS America annual meeting. Minneapolis, Minnesota, pp 15Google Scholar
  16. 16.
    Balan G, Luke S (2006) History-based traffic control. In: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems, ACM, pp 616–621Google Scholar
  17. 17.
    Jin Q, Wu G, Boriboonsomsin K, Barth M (2012) Advanced intersection management for connected vehicles using a multi-agent system approach. In: IEEE on intelligent vehicles symposium, pp 932–937Google Scholar
  18. 18.
    Desai P, Loke SW, Desai A, Singh A (2011) Multi-agent based vehicular congestion management. In: IEEE on intelligent vehicles symposiumGoogle Scholar
  19. 19.
    Gregoire J, Bonnabel S, de La Fortelle A (2013) Optimal cooperative motion planning for vehicles at intersections. In: arXiv preprint arXiv:1310.7729, pp 16
  20. 20.
    Yan F, Jia W, Dridi M (2014) A scheduling model and complexity proof for autonomous vehicle sequencing problem at isolated intersections. In: 2014 IEEE international conference on service operations and logistics, and informatics (SOLI). IEEE, pp 78–83Google Scholar
  21. 21.
    Vasirani M, Ossowski S (2009) A market-inspired approach to reservation based urban road traffic management. In: Proceedings of the 8th international conference on autonomous agents and multiagent systems-volume 1. International foundation for autonomous agents and multiagent systems, pp 617–624Google Scholar
  22. 22.
    Kamal MAS, Hayakawa T, Imura J-C, Ohata A, Aihara K (2015) A vehicle-intersection coordination scheme for smooth flows of traffic without using traffic lights. IEEE Trans Intel Transp Syst 16: 1136–1147Google Scholar

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© Springer Nature Singapore Pte Ltd. 2022

Authors and Affiliations

  1. 1.Department of Computer ScienceFaculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah UniversityFezMorocco

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