T-DBSCAN: A Spatiotemporal Density Clustering for GPS Trajectory Segmentation

Abstract

Trajectory data generated from personal or vehicle use of GPS devices can be utilized for travel analysis and traffic information service, whereas trip segmentation is a key step toward the semantic labelling of the trajectories. Two issues are difficult to deal with by the traditional density-based algorithms, i. e. multiple stops at the same spatial location with different visit times and non-consecutive point sequence for stop definition due to signal drifting. This article aims to develop a modified density-based clustering algorithm, named T-DBSCAN, by considering the time-sequential characteristics of the GPS points along a trajectory. Two new premises (i.e. state continuity within a single stop and temporal disjuncture among stops) were proposed as a theoretical basis for regulating the trajectory point selection in clustering. An empirical test was performed using a GPS-based personal travel dataset collected in the city of Shanghai to compare T-DBSCAN against DBSCAN. The results indicated that T-DBSCAN effectively improved both accuracy and computational speed in trajectory segmentation.