AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Traffic light control through agent-based coordination
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Autonomous Agents and Multi-Agent Systems
Learning in groups of traffic signals
Engineering Applications of Artificial Intelligence
Distributed traffic signal control approach based on multi-agent
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
A review of the applications of agent technology in traffic and transportation systems
IEEE Transactions on Intelligent Transportation Systems
Type-2 fuzzy logic based urban traffic management
Engineering Applications of Artificial Intelligence
Collaborative Agents for Modeling Traffic Regulation Systems
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Context-Aware ubiquitous data mining based agent model for intersection safety
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
Air pollution assessment through a multiagent-based traffic simulation
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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For the purposes of managing an urban traffic system, ahierarchical multiagent system that consists of several locallyoperating agents each representing an intersection of a trafficsystem is proposed. Local Traffic Agents (LTAs) are concerned withthe optimal performance of their assigned intersection; however,the resulting traffic light patterns may result in the failure ofthe system when examined at a global level. Therefore, supervisionis required and achieved with the use of a Coordinator TrafficAgent (CTA).A CTA provides a means by which the optimal local lightpattern can be compared against the global concerns. The patterncan then be slightly modified to accommodate the globalenvironment, while maintaining the local concerns of theintersection. Functionality of the proposed system is examinedusing two traffic scenarios: traffic accident and morning rushhour. For both scenarios, the proposed multiagent systemefficiently managed the gradual congestion of the traffic.