A Multiagent System for Optimizing Urban Traffic
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Solving Distributed Constraint Optimization Problems Using Cooperative Mediation
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Using cooperative mediation to coordinate traffic lights: a case study
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Urban Traffic Signal Control Based on Distributed Constraint Satisfaction
HICSS '08 Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Autonomous Agents and Multi-Agent Systems
A market-inspired approach to reservation-based urban road traffic management
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Dynamic modeling of a disturbance in a multi-agent system for traffic regulation
Decision Support Systems
Urban traffic control with co-fields
E4MAS'06 Proceedings of the 3rd international conference on Environments for multi-agent systems III
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The development of surface public transportation networks is a major issue in terms of ecology, economy and society. Their quality in term of punctuality and passengers services(regularity between buses) should be improved. To do so, cities often use regulation systems at junctions that grant priority to buses. However, most of them hardly take into account both public transport vehicles such as buses and private vehicle traffic. This paper proposes a bimodal urban traffic control strategy based on a multi-agent model. The objective is to improve global traffic, to reduce bus delays and to improve bus regularity in congested areas of the network. In our approach, traffic regulation is obtained thanks to communication, collaboration and negotiation between heterogeneous agents. We tested our strategy on a complex network of nine junctions. The results of the simulation are presented.