Recognizing Network Trip Patterns Using a Spatio-Temporal Vehicle Trajectory Clustering Algorithm

作者：Hong Zihan; Chen Ying; Mahmassani Hani S^*

来源：IEEE Transactions on Intelligent Transportation Systems, 2018, 19(8): 2548-2557.

DOI：10.1109/TITS.2017.2754401

• Data mining
• spatio-temporal data clustering
• dynamic network trip patterns
• vehicle trajectories
• shortest path distance

This paper presents a spatio-temporal trajectory clustering method for vehicle trajectories in transportation networks to identify heterogeneous trip patterns and explore underlying network assignment mechanisms. The proposed algorithm ST-TOPOSCAN is designed to consider both temporal and spatial information in trajectories. We adopt the time-dependent shortest-path distance measurement and take advantage of topological relations of a predefined network to discover the shared sub-paths among trajectories and construct the clusters. The proposed algorithm is implemented with a trajectory dataset obtained in the Chicago area. The results confirm the method's ability to extract and generate spatio-temporal (sub-) trajectory clusters and identify trip patterns. Extensive numerical experiments verify the method's performance and computational efficiency. Through spatio-temporal data mining, this paper contributes to exploring traffic system dynamics and advancing state-of-the-art spatio-temporal clustering for vehicle trajectories.

• 出版日期2018-8
• 单位西北大学