Visual Analytics of Taxi Trajectory Data via Topical Sub-trajectories

Abstract

GPS-based taxi trajectories contain valuable knowledge about movement behaviors for transportation and urban planning. Topic modeling is an effective tool to extract semantic information from taxi trajectories. However, previous methods generally ignore the direction of trajectories. In this paper, we employ the bigram topic model instead of traditional topic models to analyze textualized trajectories to take into account the direction information of trajectories. We further propose a modified Apriori algorithm to extract frequent sub-trajectories and use them to represent each topic as topical sub-trajectories. Finally, we design a visual analytics system with several linked views to facilitate users to interactively explore topics, sub-trajectories, and trips. We demonstrate the effectiveness of our system via case studies with Chengdu taxi trajectory data.