Nowadays, research on Intelligent Transportation System (ITS) has received many attentions due to its broad applications, such as path planning, which has become a common activity in our daily life. Besides, with the advances of Web 2.0 technologies, users are willing to share their trajectories, thus providing good resources for ITS applications. To the best of our knowledge, there is no study on the fastest path planning with multiple destinations in the literature. In this paper, we develop a novel framework, called Trajectory-based Path Finding (TPF), which is built upon a novel algorithm named Mining-based Algorithm for Travel time Evaluation (MATE) for evaluating the travel time of a navigation path and a novel index structure named Efficient Navigation Path Search Tree (ENS-Tree) for efficiently retrieving the fastest path. With MATE and ENS-tree, an efficient fastest path finding algorithm for single destination is derived. To find the path for multiple destinations, we propose a novel strategy named Cluster-Based Approximation Strategy (CBAS), to determine the fastest visiting order from specified multiple destinations. Through a comprehensive set of experiments, we evaluate the proposed techniques employed in the design of TPF and show that MATE, ENS-tree and CBAS produce excellent performance under various system conditions.
- Data mining
- Intelligent transportation system
- Path planning