An Adaptive Traffic-Calming Measure and Effectiveness Evaluation in a Large Urban Complex of Shanghai, China

Abstract

The rapid development of the motor vehicle brings convenience to our life; however, it also increases the burden on traffic networks and the environment, especially when road space is limited. Traffic calming has proved to be an effective solution for the improvement of traffic safety and travel quality. However, most traffic-calming measures are investigated and carried out without any adaptive ability. Such measures cannot adapt to changing traffic requirements. There is a mismatch between static measures and dynamic traffic. In this study, we propose an adaptive traffic-calming measure using deep reinforcement learning. Traffic volume is controlled at intersections according to the state of dynamic traffic. Then, we take a large urban complex (the Jinding nine-rectangle-grid area) in Shanghai, China, as an example. Further, based on applied static traffic-calming measures, we consider the characteristics of the nine plots, along with traffic demand, to design traffic-calming measures. Finally, the effectiveness of the measures is evaluated in SUMO (Simulation of Urban Mobility). The experimental results show that the proposed measure can increase driving speed under the speed limit and reduce traffic volume in a peak period. The results indicate that the proposed measure is an effective and novel solution for traffic calming in the large urban complex.