Intelligent Congestion Avoidance Algorithm and System -- Application of Data Vitalization

This paper proposes a novel method for the timely selection of a shortest travel time route for drivers. The problem is formulated as a path planning problem: traffic congestion avoidance in an urban traffic network using multi-source traffic information. The key idea is to compute the travel time for each road section and to dynamically select the fastest path according to the traffic. We collect traffic information from the Internet and combine this current information with historical data for path planning. This information defines traffic hotspots that could produce traffic jams on the road. An improved traffic impedance model is used for calculating the road travel time in this paper according to the traffic conditions. The improved impedance model needs to import the impact level of traffic hotspots, so we also proposed a novel time-space influence model for calculating the hotspots impedance level on the road. In order to obtain the final path, we update the operation on the queue in the SPFA algorithm for dynamic routing on stochastic traffic network. Experiments on the real scenario using Google map for navigation for drivers demonstrate a shortest travel time path effectively.