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

Abstract

Transport delays due to traffic jams are manifest 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) con- gestion, Vehicle-To-Vehicle (V2V) and Vehicle-To-Infrastructure (V2I) communication provide a means for cooperation and intelligent route management in transport networks. This article introduces the novel PCMA* algorithm (Predictive Congestion Minimization in combination with an A* based router), which provides a comprehensive framework for detection, prediction and avoidance of traffic congestion. It assumes utilization of V2X 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, and 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 to situations with no active rerouting system, but also compared to simple rerouting algorithms and more sophisticated approaches from literature.