Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Signal control using fuzzy logic
Fuzzy Sets and Systems - special issue on fuzzy sets in traffic and transport systems
Multiagent Traffic Management: A Reservation-Based Intersection Control Mechanism
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Multiagent traffic management: an improved intersection control mechanism
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Dealing with non-stationary environments using context detection
ICML '06 Proceedings of the 23rd international conference on Machine learning
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent 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
Intelligent Traffic Control Decision Support System
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Evolving control laws for a network of traffic signals
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Traffic intersections of the future
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
A multiagent approach to autonomous intersection management
Journal of Artificial Intelligence Research
Learning in groups of traffic signals
Engineering Applications of Artificial Intelligence
IEEE Transactions on Intelligent Transportation Systems
Traffic lights control with adaptive group formation based on swarm intelligence
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
IEEE Transactions on Evolutionary Computation
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As traffic congestion rises within urban centers around the world, the intelligent control of traffic signals within cities is becoming increasingly important. Previous research within the area of intelligent traffic signal control has several shortcomings, including a reliance on historical data, the use of centralized systems which cannot handle city-sized problem instances and solutions which are not capable of addressing real-world traffic scenarios (e.g., constantly varying volumes and complex network structures). The research reported here proposes algorithms capable of controlling traffic signals that rely on traffic observations made by available sensor devices and local communication between traffic lights. This solution allows signals to be updated frequently to match current traffic demand, while also allowing for significantly large problem sizes to be addressed. To evaluate the developed system, a realistic traffic model was developed using information supplied by the City of Ottawa, Canada. It was found, through simulation within the SUMO traffic simulation environment, that the proposed adaptive system resulted in higher overall network performance when compared to the current fixed signal plan controllers, which were recreated using information from the City of Ottawa. This work also includes examples of why fixed signal controllers are inferior to an adaptive control system.