Continuous Fastest Path Planning in Road Networks by Mining Real-Time Traffic Event Information

In recent years, a number of studies had been done on the issues of fastest navigation path planning due to wide applications. Most of previous studies focused on the fastest path planning by mining historical traffic logs. However, the real time traffic situations in the road network always vary continuously due to the occurrences of traffic events. Therefore, a better planning strategy should take into account the effects of traffic events to avoid the traffic congestions. In this paper, we propose a novel prediction-based method named Traffic Event Prediction Algorithm (TEPA) for mining the traffic event knowledge which can be used to predict the effects of traffic events from historical traffic logs. In addition, we propose three continuous path planning strategies for finding the fastest path according to the real time traffic information. Finally, through a series of experiments, the proposed method was shown to have excellent performance under various system conditions.