A multi agent system for urban traffic control

Traffic signal control is a system for synchronizing the timing of any number of traffic signals in a target road domain. These systems automate the process of adjusting signals to optimize traffic flow by reducing stops, overall vehicle delay and thus maximizing throughput. The study presented in this thesis proposes a new intelligent traffic light control that is quickly adaptive to changing environment. The new controller focuses on urban intersections and road lanes that are incoming to this intersection. The system inputs are traffic volumes on road lanes and the outputs are continuously changing light periods for the traffic light units in target intersections. The proposed system is based on a hierarchical multi agent model and a fuzzy controller is executed through this agent hierarchy. In addition to this local traffic light control, a reasoning engine is also integrated into the system to evaluate neighbor intersection situations. The outputs of the reasoning engine are also used for updating traffic light periods. All these work has been implemented on software basis and the results are given according to some sample scenarios. The obtained results show that the proposed dynamic signalization system outperforms the fixed time-plan based controllers and generate better vehicle flows through intersections.