Application of ADP to Intersection Signal Control
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
IEEE Transactions on Intelligent Transportation Systems
A review of the applications of agent technology in traffic and transportation systems
IEEE Transactions on Intelligent Transportation Systems
Distributed geometric fuzzy multiagent urban traffic signal control
IEEE Transactions on Intelligent Transportation Systems
Multi-policy optimization in self-organizing systems
SOAR'09 Proceedings of the First international conference on Self-organizing architectures
Type-2 fuzzy logic based urban traffic management
Engineering Applications of Artificial Intelligence
Urban arterial traffic coordination control system
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
ACM SIGAPP Applied Computing Review
Holonic multi-agent system for traffic signals control
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Hierarchical control of traffic signals using Q-learning with tile coding
Applied Intelligence
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Real-time traffic signal control is an integral part of the urban traffic control system, and providing effective real-time traffic signal control for a large complex traffic network is an extremely challenging distributed control problem. This paper adopts the multiagent system approach to develop distributed unsupervised traffic responsive signal control models, where each agent in the system is a local traffic signal controller for one intersection in the traffic network. The first multiagent system is developed using hybrid computational intelligent techniques. Each agent employs a multistage online learning process to update and adapt its knowledge base and decision-making mechanism. The second multiagent system is developed by integrating the simultaneous perturbation stochastic approximation theorem in fuzzy neural networks (NN). The problem of real-time traffic signal control is especially challenging if the agents are used for an infinite horizon problem, where online learning has to take place continuously once the agent-based traffic signal controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District of Singapore has been developed using PARAMICS microscopic simulation program. Simulation results show that the hybrid multiagent system provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as the SPSA-NN-based multiagent system as the complexity of the simulation scenario increases. Using the hybrid NN-based multiagent system, the mean delay of each vehicle was reduced by 78% and the mean stoppage time, by 85% compared to the existing traffic signal control algorithm. The promising results demonstrate the efficacy of the hybrid NN-based multiagent system in solving large-scale traffic signal control problems in a distributed manner