Adaptive Area-Based Traffic Congestion Control and Management Scheme Based on Fog Computing

Abstract

How to globally construct the effective traffic congestion control and management method on large-scale urban road network is an important research challenge. Since an urban region can be divided into many areas to be managed, grid management has great potential to improve traffic management on large-scale urban road network. In this paper, we propose an adaptive area-based traffic congestion control and management scheme, which is based on fog computing-based internet of vehicles. In our proposed scheme, an urban region is divided to many traffic management areas. Based on the real-time dynamic traffic tightness degrees and the static correlation degrees between these divided management areas, we can obtain the complete real-time regional traffic correlations of these divided management areas. Further, when a traffic congestion occurs, the fog servers use the constructed measures to control the signal lights in real time and effectively guide the traffic flow through the cooperation between these divided management areas. The experimental results show our proposed scheme can effectively implement the road guidance and traffic light control to alleviate or eliminate traffic congestion when a traffic congestion occurs.