Hotspots Extraction Based on Spatial-Temporal Trajectory Data

Trajectory clustering algorithm can be widely used in traffic management. Finding the vehicle trajectory hotspots by using trajectory clustering algorithm has important guiding significance for traffic planning and management of traffic travel. Current trajectory clustering algorithms are mostly measured by spatial similarity, which cannot reflect the division of trajectory hotspots in different time periods. In response to the above problems, this paper proposes a hotspot region extraction algorithm for spatio-temporal trajectory, combined with the factor of time. Firstly, the traditional density peak clustering algorithm and the density calculation method are improved by considering the linear and nonlinear parts of the calculated density. At the same time, the method of choosing cluster center is modified to enable it to automatically select the cluster center. On the basis of the above, we propose a clustering fusion algorithm to filter inappropriate clusters and redundant clusters and use the DB index to detect the division results. The experimental results show that our algorithm can extract the hot spots of spatio-temporal trajectories more effectively than the traditional clustering algorithms. © 2019, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.