Applied Intelligence

, Volume 39, Issue 4, pp 793–804 | Cite as

A real-time transportation prediction system

  • Haiguang Li
  • Zhao Li
  • Robert T. White
  • Xindong Wu


In recent years, the use of advanced technologies such as wireless communication and sensors in intelligent transportation systems has made a significant increase in traffic data available. With this data, traffic prediction has the ability to improve traffic conditions and to reduce travel delays by facilitating better utilization of available capacity. This paper presents a real-time transportation prediction system named VTraffic for Vermont Agencies of Transportation by integrating traffic flow theory, advanced sensors, data gathering, data integration, data mining and visualization technologies to estimate and visualize the current and future traffic. In the VTraffic system, acoustic sensors were installed to monitor and to collect real-time data. Reliable predictions can be obtained from historical data and be verified and refined by the current and near future real-time data.


Sensors Intelligent transportation system Traffic prediction Real-time Data mining Visualization 


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Haiguang Li
    • 1
  • Zhao Li
    • 2
  • Robert T. White
    • 3
  • Xindong Wu
    • 1
  1. 1.The University of VermontBurlingtonUSA
  2. 2.TCL Research AmericaSanta ClaraUSA
  3. 3.State of Vermont AOTMontpelierUSA

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