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Recognizing Network Trip Patterns Using a Spatio-Temporal Vehicle Trajectory Clustering Algorithm
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DOI：10.1109/TITS.2017.2754401.png)
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Abstract
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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.
Keywords:
Data mining
spatio-temporal data clustering
dynamic network trip patterns
vehicle trajectories
shortest path distance
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