A Queue-Length-Based Detection Scheme for Urban Traffic Congestion by VANETs

Traffic congestion has become a global problem in urban areas, resulting in a great waste of time and fuel every year. Thus, the real-time detection for road congestion becomes a great challenge for both scientists and engineers. Recently, the technology of vehicular ad-hoc networks (VANETs) has been utilized to alleviate this problem, and becomes an important component of the intelligent transportation system (ITS). In this paper, we propose a novel detection scheme for traffic congestion based on the communication between vehicles and road side unit (RSU). In our scheme, the length of waiting queue during red light acts as a measure index to evaluate the traffic status of intersections. In order to detect the queue length timely and accurately, a restricted greedy forwarding strategy is put forward to transmit the status information from tail vehicle by multihop broadcast. According to the driver-perceived performance, a congestion index is built to describe the traffic condition of detected intersections. Finally, all the proposed approaches are validated in a realistic scenario. The simulation results show that our scheme can detect urban traffic congestion effectively.