Visual analytics of taxi trajectory data via topical sub-trajectories

• Published in: Visual informatics (Online) Vol. 3; no. 3; pp. 140 - 149
• Main Authors: Liu, Huan, Jin, Sichen, Yan, Yuyu, Tao, Yubo, Lin, Hai
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2019; Elsevier
• Subjects: Topic model; Trajectory pattern mining; Trajectory visualization; Visual analytics; Trajectory visualization; Trajectory pattern mining; Visual analytics; Topic model
• ISSN: 2468-502X; 2468-502X
• DOI: 10.1016/j.visinf.2019.10.002

GPS-based taxi trajectories contain valuable knowledge about movement patterns for transportation and urban planning. Topic modeling is an effective tool to extract semantic information from taxi trajectory data. However, previous methods generally ignore trajectory directions that are important in the analysis of movement patterns. In this paper, we employ the bigram topic model rather than traditional topic models to analyze textualized trajectories and consider the direction information of trajectories. We further propose a modified Apriori algorithm to extract topical sub-trajectories and use them to represent each topic. Finally, we design a visual analytics system with several linked views to facilitate users to interactively explore movement patterns from topics and topical sub-trajectories. The case studies with Chengdu taxi trajectory data demonstrate the effectiveness of the proposed system.