A Novel Dynamic En-Route Decision Real-Time Route Guidance Scheme in Intelligent Transportation Systems

Abstract

In an intelligence transportation system (ITS), to increase traffic efficiency, a number of dynamic route guidance schemes have been designed to assist drivers in determining the optimal route for their travels. In order to determine optimal routes, it is critical to effectively predict the traffic condition of roads along the guided routes based on real-time traffic information to mitigate traffic congestion and improve traffic efficiency. In this paper, we propose a Dynamic En-route Decision real-time Route guidance (DEDR) scheme to effectively mitigate road congestion caused by the sudden increase of vehicles and reduce travel time. Particularly, DEDR considers real-time traffic information generation and transmission. Based on the shared traffic information, DEDR introduces Trust Probability to predict traffic conditions and dynamically en-route determine alternative optimal routes. In addition, DEDR considers multiple metrics to comprehensively assess traffic conditions and drivers can determine optimal route with individual preference of these metrics during travel. DEDR also considers effects of external factors (e.g., Bad weather, incidents, etc.) on traffic conditions. Through a combination of extensive theoretical analysis and simulation experiments, our data shows that DEDR can greatly increase the efficiency of an ITS in terms of great time efficiency and balancing efficiency in comparison with existing schemes.