Analyzing the Impact of Anticipatory Vehicle Routing on the Network Performance

Abstract

Traffic congestion causes lost in time, fuel consumption and frustration of the drivers. Route guidance systems are considered as the most feasible solution to alleviate traffic congestion. However, existing route navigation systems provide route guidance based only on real-time traffic information, without considering the future evolvement of traffic in the network. These strategies, known as reactive routing strategies, react only when congestion has already occurred and do not prevent congestion from happening. Therefore, researchers have been investigating in anticipatory route guidance, to enable the integration of traffic prediction into route recommendation. The anticipatory route guidance is expected to improve the effectiveness of the route guidance systems, however its effects on the overall traffic on the network are unclear. The main aim of this paper is to analyze the impacts of the anticipatory routing strategy on the network performance. To achieve this, the performance of the system during anticipatory routing strategy is compared to current reactive routing strategy. The models of the routing strategies are built and tested in a microscopic simulation environment and the results are compared using suitable network performance indicators, such as average travel time, total delay time and the effects of anticipatory routing on different vehicle types. Evaluation results show that the anticipatory routing strategy can improve the network performance, but only for a limited penetration rate of the vehicles equipped with navigation system based on the predictive traffic information, the average travel time compared to reactive routing is reduced by up to 20 % in the best scenario and the vehicles with the predictive navigation system are the ones to profit the most from the predictive routing strategy.