Hot Passenger Routes Mining Based on Spatial-temporal Similarity Clustering

The taxi passenger trajectory can be exploited to discover the vehicle running state and the law of the travel behaviors of urban citizens. The mining of hot passenger routes has important value for traffic management and planning, citizens' behavior pattern discovery and taxi passenger recommendation. In this paper, a mining algorithm of hot passenger routes based on spatial-temporal similarity clustering is proposed from taxi passenger trajectory generated by over 3 000 taxis for one week in Lanzhou, China. Firstly, the passenger trajectory and its core trajectory are extracted according to the GPS trajectory data of taxi. Then, the spatial similarity, temporal similarity and spatial-temporal similarity of the core trajectory are calculated based on the proposed similarity measurement algorithm. The passenger trajectory is clustered using the DBSCAN clustering algorithm. Finally, the spatial distribution of hot passenger routes is obtained according to the clustering results. The differences of hot passenger routes between weekday and weekend are analyzed. Experimental results show that the proposed mining algorithm can effectively and quickly find the distribution of hot passenger routes. Copyright © 2019 by Science Press.