Multi-agent based traffic light management for privileged lane

  • Ikidid Abdelouafi 
  • Abdelaziz El Fazziki
  • a,b Cadi Ayyad University, Computer Science Dept, Computer Systems Engineering Laboratory, Marrakesh, Morocco
Cite as
Abdelouafi I., El Fazziki A. (2020). Multi-agent based traffic light management for privileged lane. Proceedings of the 8th International Workshop on Simulation for Energy, Sustainable Development & Environment (SESDE 2020), pp. 1-6. DOI: https://doi.org/10.46354/i3m.2020.sesde.001

Abstract

To optimize the travel time of priority vehicles (PV) and fluidize road traffic in Moroccan cities, we propose in this paper a control system to manage traffic at signalized intersections in urban areas, based on a multi-agent technology and fuzzy logic. the traffic system flow is divided into two types of vehicles; priority and non-priority vehicles, and a group of lanes assigned to each type, the ordinary vehicles can exploit only the ordinary lanes, while the PV may use both the priority and the ordinary lanes. This approach aims to favor emergency vehicles and promote collective modes of transport, it acts on the durations of the traffic light phases to control the different flows. We proposed a decentralized system of regulation based on the supervision of each lane which connects two intersections to build a local view of the crossroad, and intelligent cooperation between neighboring intersections to develop an overview of the environment. The regulation and prioritization decisions are made in real-time by fuzzy logic inference, communication, collaboration, and coordination between different agents. The performance of the proposed system is validated and designed in ANYLOGIC simulator, using a virtual road network that represents a section of the Marrakesh network.

References

  1. Abu-Taieh, Evon M.O., and Asim Abdel Rahman El Sheikh. 2007. “Commercial Simulation Packages: A Comparative Study.” International Journal of Simulation: Systems, Science and Technology 8(2): 66–76
  2. Balaji, P. G., and D. Srinivasan. 2011. “Type-2 Fuzzy Logic Based Urban Traffic Management.” Engineering Applications of Artificial Intelligence 24(1): 12–22. http://dx.doi.org/10.1016/j.engappai.2010.08.007.
  3. Bazzan, Ana Lucia C. 2005. “A Distributed Approach for Coordination of Traffic Signal Agents.” International Conference on Autonomous Agents and Multi-Agent Systems 10(1): 131–64.
    http://www.springerlink.com/index/10.1007/s10458-004-6975-9.
  4. Bhouri, Neila, P Lotito, Neila Bhouri, and P Lotito Régulation. 2017. “Régulation Du Trafic Urbain Multimodal Avec Priorité Pour Les Transports En Commun.”
  5. Dorri, Ali, Salil S. Kanhere, and Raja Jurdak. 2018. “Multi-Agent Systems: A Survey.” IEEE Access 6: 28573–93.
  6. Ferber, Jacques, and Olivier Gutknecht. 1998. “A MetaModel for the Analysis and Design of Organizations in Multi-Agent Systems.” Proceedings - International Conference on Multi Agent Systems, ICMAS 1998: 128–35.
  7. Garcia-Nieto, Jose, Javier Ferrer, and Enrique Alba. 2014. “Optimising Traffic Lights with Metaheuristics: Reduction of Car Emissions and Consumption.” Proceedings of the International Joint Conference on Neural Networks: 48–54.
  8. Ministry of Equipment Transport and Logistics - Morocco, Morocco. 2017. “Recueil Du Trafic Routier 2017.”
  9. Sautriau, F. 1968. “Cahier Technique 2.”
  10. Soetanto, Danny Prabowo. 2000. “Implementing Fuzzy Logic in Determining Selling Price.” Jurnal Teknik Industri 2(1): 43–52.
  11. Teknomo, Kardi. 2006. “Application of Microscopic Pedestrian Simulation Model.” Transportation Research Part F: Traffic Psychology and Behaviour 9(1): 15–27.
  12. Tlig, Mohamed, and Neïla Bhouri. 2011. “A MultiAgent System for Urban Traffic and Buses Regularity Control.” Procedia - Social and Behavioral Sciences 20: 896–905. http://dx.doi.org/10.1016/j.sbspro.2011.08.098.
  13. Xiao Xiong Weng, Shu Shen Yao, and Xue Feng Zhu. 2005. “Architecture of Multi-Agent System for Traffic Signal Control.” (December): 2199–2204.