Adapting Simple Approach for Traffic Light Intersection

Abstract

Traffic congestion is one of the major problems facing drivers some over the world, especially in the urban road network, where lost time and fuel consumption are major concern. Economic, social and environmental problems arise due to ineffective intersection control. Therefore, Intelligent Transportation Systems (ITS) are important in reducing congestion and accidents, increasing traffic safety and reducing air pollution. With the tremendous advances in mobility and networking technology, Vehicular Ad hoc NETworks (VANETs) have attracted researchers from academia and industry. Traffic Signal Control (TSC) can be self-regulating and adaptive to changes in traffic conditions using VANET technology and intelligent control algorithm as VANET technology provides comprehensive information on vehicles approaching intersections. Communication between vehicles allows the exchange of information to improve traffic safety. One of the types of VANET communication is V2X as it can send, receive and rebroadcast safety messages, so TSC communication with V2X is a very effective solution to the problem of traffic congestion. In this thesis, an intersection in the city of Ramadi was studied and analyzed by collecting real-time traffic information in the road network surrounding the intersection. Then, a dynamic scheduling algorithm was proposed based on the VANET scenario, which schedules competing flows in traffic. The proposed algorithm aims to increase traffic flow and performance of the intersection by taking advantage of excess green time in order to reduce the waiting time and delay of mobile vehicles depending on vehicles information (speed, location, and number of vehicles) under different coverage area. Instead of using the fixed maximum green time currently used at the intersection, and depending on the coverage area and vehicles arrival, an adaptive green time method has been proposed and investigated. The coverage area and developed algorithm are simulated in SUMO simulator with MATLAB through TRACI tool to obtain the vi results. The results showed a significant improvement in traffic throughput and reduced delay time by different percentages (the lowest was 7.322% and the highest was 16.902% ) according to the difference in the coverage area compared to the traditional (i.e. fixed) cycle time currently used in our case study