SUN Guodao, LI Si, CAO Dizhou, LIU Chunhui, JIANG Xiaorui, LIANG Ronghua. DiffusionInsighter: Visual Analysis of Traffic Diffusion Flow Patterns[J]. Chinese Journal of Electronics, 2018, 27(5): 942-950. doi: 10.1049/cje.2017.12.008
Citation: SUN Guodao, LI Si, CAO Dizhou, LIU Chunhui, JIANG Xiaorui, LIANG Ronghua. DiffusionInsighter: Visual Analysis of Traffic Diffusion Flow Patterns[J]. Chinese Journal of Electronics, 2018, 27(5): 942-950. doi: 10.1049/cje.2017.12.008

DiffusionInsighter: Visual Analysis of Traffic Diffusion Flow Patterns

doi: 10.1049/cje.2017.12.008
Funds:  This work is supported by the National Natural Science Foundation of China (No.61602409), Zhejiang Provincial NSFC (No.LR14F020002), joint project Data-Driven Intelligent Transportation between China and Europe announced by the Ministry of Science and Technology of China (No.SQ2013ZOC200020), and the Open Projects Program of Key Laboratory of Ministry of Public Security based on Zhejiang Police College (No.2016DSJSYS003).
More Information
  • Corresponding author: LIANG Ronghua (corresponding author) received the Ph.D. degree in computer science from Zhejiang University in 2003. He worked as a research fellow at the University of Bedfordshire, UK, from April 2004 to July 2005 and as a visiting scholar at the University of California, Davis, US, from March 2010 to March 2011. He is currently a professor of Computer Science and Dean of College of Information Engineering, Zhejiang University of Technology, China. His research interests include visual analytics, computer vision, and medical visualization. (Email:rhliang@zjut.edu.cn)
  • Received Date: 2017-03-16
  • Rev Recd Date: 2017-07-09
  • Publish Date: 2018-09-10
  • Traffic jam has become a severe urban problem to most metropolises in the world. How to understand and resolve these traffic problems has become a global issue. In the new era of big data, visualization and analysis with traffic-related data are increasingly appreciated. This paper presents DiffusionInsighter, a web-based visual traffic analysis system, that allows users to explore the traffic flow and diffusion patterns with different spatial and temporal granularity. The DiffusionInsighter first applies a visual data cleaning and filtering component to remove dirty data and remain available ones for further analysis. A set of carefully designed interaction and visualization tools including geographical view, pixel map view, chord diagram and network diffusion view is proposed in the DiffusionInsighter to support level-of-detail exploration of diffusion patterns of the traffic flow. Different views are collaborated together and are integrated into geographic map. A series of real-life case studies are conducted using a large GPS trajectory dataset of taxis in Hangzhou.
  • loading
  • D. Helbing, et al., Traffic and Granular Flow99: Social, Traffic and Granular Dynamics, Springer Science & Business Media, Berlin, Germany, pp.481-0487, 2000.
    W. Chen, F. Guo and F.Y. Wang, “A survey of traffic data visualization”, IEEE Transactions on Intelligent Transportation Systems, Vol.16, No.6, pp.2970-2984, 2015.
    R. Krueger, G.D. Sun, F. Beck, et al., “TravelDiff: Visual comparison analytics for massive movement patterns derived from Twitter”, 2016 IEEE Pacific Visualization Symposium (PacificVis), Taipei, China, pp.176-183, 2016.
    G.D. Sun, R.H. Liang, H.M. Qu, et al., “Embedding spatiotemporal information into maps by route-zooming”, IEEE Transactions on Visualization and Computer Graphics, Vol.23, No.5, pp.1506-1519, 2017.
    G. Andrienko, N. Andrienko, U. Demsar, et al., “Space, time and visual analytics”, International Journal of Geographical Information Science, Vol.24, No.10, pp.1577-1600, 2010.
    G. Andrienko, N. Andrienkoand and M. Heurich, “An eventbased conceptual model for context-aware movement analysis”, International Journal of Geographical Information Science, Vol.25, No.9, pp.1347-1370, 2011.
    Z. Wang, M. Lu, X.Y. Yuan, et al., “Visual traffic jam analysis based on trajectory data”, IEEE Transactions on Visualization and Computer Graphics, Vol.19, No.12, pp.2159-2168, 2013.
    Z. Wang, T. Ye, M. Lu, et al., “Visual exploration of sparse traffic trajectory data”, IEEE transactions on visualization and computer graphics, Vol.20, No.12, pp.1813-1822, 2014.
    H. Liu, Y. Gao, L. Lu, et al., “Visual analysis of route diversity”, Proceedings of the IEEE Symposium on Visual Analytics Science and Technology, Providence, RI, USA, pp.171-180, 2011.
    D.Q. Zhang, L. Sun, B. Li, et al., “Understanding taxi service strategies from taxi GPS traces”, IEEE Transactions on Intelligent Transportation Systems, Vol.16, No.1, pp.123-135, 2015.
    W. Kim, B.-J. Choi, E.-K. Hong, et al., “A taxonomy of dirty data”, Data Mining and Knowledge Discovery, Vol.7, No.1, pp.81-99, 2003.
    D.A. Keim, “Designing pixel-oriented visualization techniques: Theory and applications”, IEEE Transactions on visualization and computer graphics, Vol.6, No.1, pp.59-78, 2002.
    Hellerstein, M. Joseph and P.J. Haas, “Online aggregation”, ACM SIGMOD Record, Vol.26, No.2, pp.171-182, 1997.
    R. Twiddy, J. Cavallo and S.M. Shiri, “Restorer: A visualization technique for handling missing data”, Proceedings of the conference on Visualization ’94, Washington, DC, USA, pp.212-216, 1994.
    A.M. MacEachren, A. Robinson, S. Hopper, et al., “Visualizing geospatial information uncertainty: What we know and what we need to know”, Cartography and Geographic Information Science, Vol.32, No.3, pp.139-160, 2005.
    H. Guo, et al., “TripVista: Triple perspective visual trajectory analytics and its application on microscopic traffic data at a road intersection”, Proceedings of the IEEE Pacific Visualization Symposium, Vol.18, No.1, pp.163-170, 2011.
    J.F. Zhao, P. Forer and A.S. Harvey, “Activities, ringmaps and geovisualization of large human movement fields”, Information Visualization, Vol.7, No.3, pp.198-209, 2008.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (205) PDF downloads(357) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return