Increased Traffic Flow Through Node-Based Bottleneck Prediction and V2X Communication

Transport delays due to traffic jams are manifested in many urban areas worldwide. To make road traffic networks more efficient, intelligent transport services are currently being developed and deployed. In order to mitigate (or even avoid) congestion, vehicle-to-vehicle and vehicle-to-infrastructure communications provide a means for cooperation and intelligent route management in transport networks. This paper introduces the novel predictive congestion minimization in combination with an A*-based router (PCMA*) algorithm, which provides a comprehensive framework for detection, prediction, and avoidance of traffic congestion. It assumes utilization of vehicle-to-X communication for transmission of contemporary vehicle data such as route source and destination or current position, as well as for provision of the routing advice for vehicles. PCMA* further contains a component for intelligent selection of vehicles to be rerouted in case of a congestion, as well as an A*-based routing algorithm, taking into consideration the current road conditions and predicted future congestion. We prove the performance by dynamic microscopic traffic simulations in a real-world and an artificial road network scenario. Due to the well-performing prediction, the results reveal substantial advantages in terms of time and fuel consumption compared not only with situations with no active rerouting system but also with simple rerouting algorithms and more sophisticated approaches from literature.