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A real-time transportation prediction system

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Abstract

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.

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Notes

  1. There are 56 sensors deployed on I89 and I91, 28 for each. The location details can be found at http://cs.uvm.edu/~hli/www/traffic/html/sensorlocations.htm.

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Authors and Affiliations

  1. The University of Vermont, Burlington, VT, 05405, USA

    Haiguang Li & Xindong Wu

  2. TCL Research America, Santa Clara, CA, 95054, USA

    Zhao Li

  3. State of Vermont AOT, Montpelier, VT, 05633, USA

    Robert T. White

Authors
  1. Haiguang Li
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  2. Zhao Li
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  3. Robert T. White
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  4. Xindong Wu
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Corresponding author

Correspondence to Haiguang Li.

Additional information

This work is supported by Vermont Agencies of Transportation under grant No. 000025425. An earlier version of this paper was presented at the 25th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems.

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Li, H., Li, Z., White, R.T. et al. A real-time transportation prediction system. Appl Intell 39, 793–804 (2013). https://doi.org/10.1007/s10489-012-0409-1

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