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A new methodology is developed to discover and analyze the hidden knowledge of massive taxi trajectory data within a city. This approach creatively transforms the geographic coordinates (i.e. latitude and longitude) to street names reflecting contextual semantic information. Consequently, the movement of each taxi is studied as a document consisting of the taxi traversed street names, which enables semantic analysis of massive taxi data sets as document corpora. Hidden themes, namely taxi topics, are identified through textual topic modeling techniques. The taxi topics reflect urban mobility patterns and trends, which are displayed and analyzed through a visual analytics system. The system integrates interactive visualization tools, including taxi topic maps, topic routes, street clouds and parallel coordinates, to visualize the probability-based topical information. Urban planners, administration, travelers, and drivers can conduct their various knowledge discovery tasks with direct semantic and visual assists. The effectiveness of this approach is illustrated by case studies using a large taxi trajectory data set acquired from 21, 360 taxis in a city.
Trajectory, Cities and towns, Semantics, Global Positioning System, Visualization, Data visualization, Roads,Latent Dirichlet Analysis, Taxi Trajectories, Semantic Transformation, Clustering, Topic Modeling
Ding Chu, David A. Sheets, Ye Zhao, Yingyu Wu, Jing Yang, Maogong Zheng, George Chen, "Visualizing Hidden Themes of Taxi Movement with Semantic Transformation", Visualization Symposium, IEEE Pacific, vol. 00, no. , pp. 137-144, 2014, doi:10.1109/PacificVis.2014.50
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