An Evolutionary Fuzzy System for Coordinated and Traffic Responsive Ramp Metering
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 3 - Volume 3
Transportation security decision support system for emergency response: A training prototype
Decision Support Systems
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
POP-TRAFFIC: a novel fuzzy neural approach to road traffic analysis and prediction
IEEE Transactions on Intelligent Transportation Systems
An evolutionary multi-agent system for database query optimization
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task, which requires significant expert knowledge and experience. Also, the application of a control action for solving a local traffic problem could create traffic congestion at different locations in the network because of the strong interrelations between traffic situations at different locations of a road network. Therefore, coordination of control strategies is required to make sure that all available control actions serve the same objective. In this paper, an Intelligent Traffic Control System (ITCS) based on a coordinated-agent approach is proposed to assist the human operator of a road traffic control centre to manage the current traffic state. In the proposed system, the network is divided into sub-networks, each of which has its own associated agent. The agent of the sub-network with an incident reacts with other affected agents in order to select the optimal traffic control action, so that a globally acceptable solution is found. The agent uses an effective way of calculating the control action fitness locally and globally. The capability of the proposed ITCS has been tested for a case study of a part of the traffic network in the Riyadh city of Saudi Arabia. The obtained results show its ability to identify the optimal global control action.