TrajGraph : A Graph-Based Visual Analytics Approach to Studying Urban Network Centralities Using Taxi Trajectory Data

We propose TrajGraph, a new visual analytics method, for studying urban mobility patterns by integrating graph modeling and visual analysis with taxi trajectory data. A special graph is created to store and manifest real traffic information recorded by taxi trajectories over city streets. It conveys...

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Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1998. - 22(2016), 1 vom: 24. Jan., Seite 160-9
1. Verfasser: Huang, Xiaoke (VerfasserIn)
Weitere Verfasser: Zhao, Ye, Yang, Jing, Zhang, Chong, Ma, Chao, Ye, Xinyue
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2016
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article Research Support, U.S. Gov't, Non-P.H.S.
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520 |a We propose TrajGraph, a new visual analytics method, for studying urban mobility patterns by integrating graph modeling and visual analysis with taxi trajectory data. A special graph is created to store and manifest real traffic information recorded by taxi trajectories over city streets. It conveys urban transportation dynamics which can be discovered by applying graph analysis algorithms. To support interactive, multiscale visual analytics, a graph partitioning algorithm is applied to create region-level graphs which have smaller size than the original street-level graph. Graph centralities, including Pagerank and betweenness, are computed to characterize the time-varying importance of different urban regions. The centralities are visualized by three coordinated views including a node-link graph view, a map view and a temporal information view. Users can interactively examine the importance of streets to discover and assess city traffic patterns. We have implemented a fully working prototype of this approach and evaluated it using massive taxi trajectories of Shenzhen, China. TrajGraph's capability in revealing the importance of city streets was evaluated by comparing the calculated centralities with the subjective evaluations from a group of drivers in Shenzhen. Feedback from a domain expert was collected. The effectiveness of the visual interface was evaluated through a formal user study. We also present several examples and a case study to demonstrate the usefulness of TrajGraph in urban transportation analysis 
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700 1 |a Zhao, Ye  |e verfasserin  |4 aut 
700 1 |a Yang, Jing  |e verfasserin  |4 aut 
700 1 |a Zhang, Chong  |e verfasserin  |4 aut 
700 1 |a Ma, Chao  |e verfasserin  |4 aut 
700 1 |a Ye, Xinyue  |e verfasserin  |4 aut 
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