Routing vehicles on smart grid to reduce distance traveled and customer service time based on Gray Wolf algorithm

The intelligent transportation system includes a large number of traffic sensors that collect high volumes of data in an effort to provide information to support and improve traffic management operations. Due to the high volume of traffic, classic methods of traffic control are not able to satisfy the requirements of the dynamic and dynamic nature of traffic. The purpose of the traffic control system is to efficiently manage the transport resources so that changes in traffic status can be managed. By using intelligent agents to automatically sense traffic changes and perform appropriate actions by referring to knowledge base and Metaheuristic algorithms, the traffic control system can be effectively managed so that the delay is as minimal as possible and the distance traveled by the vehicles to provide the service to Customers minimized. This thesis deals with optimal routing of vehicles in the Intelligent Transportation Network by considering community patterns of behavior for requesting vehicles and applying gray wolf optimization algorithm. The simulation results show that the proposed algorithm outperforms the existing methods in terms of service time and total distance traveled.

کلیدواژه ها:

intelligent transportation network ، traffic control ، optimal routing of vehicles ، Gray Wolf Optimization