An adaptive and VANETs-based Next Road Re-routing system for unexpected urban traffic congestion avoidance

Abstract

Unexpected road traffic congestion caused by en-route events, such as car crashes, road works, unplanned parades etc., is a real challenge in today's urban road networks as it considerably increases the drivers' travel time and decreases travel time reliability. To face this challenge, this paper extends our previous work named Next Road Rerouting (NRR) by designing a novel vehicle rerouting strategy that can adapt itself to the sudden change of urban road traffic conditions. This is achieved through a smart calibration of the algorithmic and operational parameters of NRR without any intervention from traffic managers. Specifically, a coefficient of variation based method is used to assign weight values to three factors in the routing cost function of NRR, and the k-means algorithm is applied periodically to choose the number of NRR enabled agents needed. This adaptive-NRR (a-NRR) strategy is supported by vehicular ad-hoc networks (VANETs) technology as this latter can provide rich traffic information at much higher update frequency and much larger coverage than induction loops used in the previously proposed static NRR. Simulation results show that in the city center area of TAPASCologne scenario, compared to the existing vehicle navigation system (VNS) and static NRR, our adaptive-NRR can achieve considerable gain in terms of trip time reduction and travel time reliability improvement.