A dynamic vehicular traffic control using ant colony and traffic light optimization

Abstract

Vehicle traffic congestion problem in urban areas due to increased number of vehicles has received increased attention from industries and universities researchers. This problem not also affects the human life in economic matters such as time and fuel consumption, but also affects it in health issues by increasing CO2 and greenhouse gases emissions. In this paper, a novel cellular ant-based algorithm combined with intelligent traffic lights based on streets traffic load condition has been proposed. In the proposed method road network will be divided into different cells and each vehicle will guide through the less traffic path to its destination using Ant Colony Optimization (ACO) in each cell. Moreover, a new method for traffic lights optimization is proposed in order to mitigate the traffic congestion at intersections. Two different scenarios have been performed through NS2 in order to evaluate our traffic lights optimization method. Based on obtained results, vehicles average speed, their waiting time and number of stopped vehicles at intersections are improved using our method instead of using usual traffic lights.