Proactive Vehicular Traffic Rerouting for Lower Travel Time

Abstract

Abstract-Traffic congestion causes driver frustration and costs billions of dollars annually in lost time and fuel consumption. This paper presents five traffic rerouting strategies designed to be incorporated in a cost-effective and easily deployable vehicular traffic guidance system that reduces travel time. The proposed strategies proactively compute individually tailored rerouting guidance to be pushed to vehicles when signs of congestion are observed on their route. The five proposed strategies are the dynamic shortest path (DSP), the A * shortest path with repulsion (AR * ), the random k shortest path (RkSP), the entropy-balanced kSP (EBkSP), and the flow-balanced kSP (FBkSP). Extensive simulation results show that the proposed strategies are capable of reducing the travel time as much as a state-of-the-art dynamic traffic assignment (DTA) algorithm while avoiding the issues that make DTA impractical, such as the lack of scalability and robustness, and high computation time. Furthermore, the variety of proposed strategies allows tuning the system to different levels of tradeoffs between rerouting effectiveness and computational efficiency. In addition, the proposed traffic guidance system can significantly improve the traffic even if many drivers ignore the guidance or if the system adoption rate is relatively low.

Next Road Rerouting: A Multiagent System for Mitigating Unexpected Urban Traffic Congestion

“…Clearly increasing the number of vehicles using selfish rerouting has a very negative impact on performance. This is consistent with the results in [28] and in line with Braess's paradox [32].…”

Traffic Management for Smart Cities

“…2. Re-routing the traffic to improve system performance, either by providing realtime information direct to the car drivers [84], or by using variable message signs. 3.…”

Real-time urban traffic amount prediction models for dynamic route guidance systems

“…The selection level, denoted as l, needs to be properly chosen to mitigate congestion without triggering secondary congestion on popular alternative routes [7]. The service provider then computes the shortest alternative route for selected vehicles using the Dijkstra algorithm based on current travel time on each road.…”

“…In the figures, we also indicate 95% confidence intervals. The selection level l is set to 3, as it produces good results with moderate computation [7]. The penetration rate is 100%; that is, all vehicles are subscribed to the route guidance service and thus can periodically receive route guidance.…”

“…Since it is not always possible to reduce the number of trips or persuade drivers to change their travel mode, distributing traffic through route guidance is considered a most feasible, effective and economic solution to urban road traffic congestion. Consequently, the route guidance system (RGS) has attracted great research interest from the government [4], companies [1,5,6] and research institutes [7] for many years. In our daily life, RGS has been widely used to facilitate driving either in unfamiliar or familiar environment through providing turn-by-turn route navigation or recommending real-time optimal route information.…”