Paper Info
Title
TrajGraph: A Graph-Based Visual Analytics Approach to Studying Urban Network Centralities Using Taxi Trajectory Data
Abstract
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.
Year
DOI
Venue
2016
10.1109/TVCG.2015.2467771
Visualization and Computer Graphics, IEEE Transactions
Keywords
DocType
Volume
data visualisation,graph theory,interactive systems,public transport,road traffic,town and country planning,traffic information systems,China,Pagerank,Shenzhen,TrajGraph,betweenness,city streets,city traffic patterns,graph analysis algorithms,graph centralities,graph modeling,graph partitioning algorithm,graph-based visual analytics approach,interactive multiscale visual analytics,map view,node-link graph view,real traffic information,region-level graphs,street-level graph,taxi trajectory data,temporal information view,urban mobility patterns,urban network centralities,urban regions,urban transportation analysis,urban transportation dynamics,visual analysis,visual interface,Centrality,Graph based visual analytics,Taxi trajectories,Transportation assessment,Urban network
Conference
22
Issue
ISSN
Citations 
1
1077-2626
34
PageRank 
References 
Authors
0.88
22
4
Name
Order
Citations
PageRank
Xiaoke Huang1461.70
Ye Zhao243129.42
Jing Yang39427.54
Chong Zhang45813.85