Visual Exploratory Data Analysis of Traffic Volume

  • Weiguo Han
  • Jinfeng Wang
  • Shih-Lung Shaw
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)


Beijing has deployed Intelligent Transportation System (ITS) monitoring devices along selected major roads in the core urban area in order to help relieve traffic congestion and improve traffic conditions. The huge amount of traffic data from ITS originally collected for the control of traffic signals can be a useful source to assist in transportation designing, planning, managing, and research by identifying major traffic patterns from the ITS data. The importance of data visualization as one of the useful data mining methods for reflecting the potential patterns of large sets of data has long been recognized in many disciplines. This paper will discuss several comprehensible and appropriate data visualization techniques, including line chart, bi-directional bar chart, rose diagram, and data image, as exploratory data analysis tools to explore traffic volume data intuitively and to discover the implicit and valuable traffic patterns. These methods could be applied at the same time to gain better and more comprehensive insights of traffic patterns and data relationships hidden in the massive data set. The visual exploratory analysis results could help transportation managers, engineers, and planners make more efficient and effective decisions on the design of traffic operation strategies and future transportation planning scientifically.


Traffic Flow Traffic Volume Traffic Signal Intelligent Transportation System Transportation Engineer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Weiguo Han
    • 1
  • Jinfeng Wang
    • 1
  • Shih-Lung Shaw
    • 2
  1. 1.Institute of Geographic Sciences & Natural Resources ResearchBeijingChina
  2. 2.Department of GeographyUniversity of TennesseeKnoxvilleUSA

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