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A Self-Learning Short-Term Traffic Forecasting System

Author

Listed:
  • Jiasong Zhu

    (Department of Transportation Engineering, Faculty of Civil Engineering, Shenzhen University, Nanhai Road 3688, Shenzhen, Guangdong, China)

  • Anthony Gar-On Yeh

    (Department of Urban Planning and Design, The University of Hong Kong, Pokfulam Road, Hong Kong SAR)

Abstract

A reliable and accurate short-term traffic forecasting system is crucial for the successful deployment of any intelligent transportation system. A lot of forecasting models have been developed in recent years but none of them could consistently outperform the others. In real-world applications, traffic forecasting accuracy can be affected by a lot of factors. Impacts of long-term changes to traffic patterns to short-term traffic forecasting are profound and this can easily make an existing forecasting system outdated. Therefore, it is very important for forecasting systems to detect long-term changes in traffic patterns and make updates accordingly. This paper presents a new forecasting mechanism, in which a dynamic hybrid approach is taken and self-learning ability is enhanced. Results of a case study show the proposed approach is feasible in enhancing the adaptability of traffic forecasting systems.

Suggested Citation

  • Jiasong Zhu & Anthony Gar-On Yeh, 2012. "A Self-Learning Short-Term Traffic Forecasting System," Environment and Planning B, , vol. 39(3), pages 471-485, June.
    • Handle: RePEc:sae:envirb:v:39:y:2012:i:3:p:471-485
    DOI: 10.1068/b36174
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    References listed on IDEAS

    as
    1. Satu Innamaa, 2005. "Short-Term Prediction of Travel Time using Neural Networks on an Interurban Highway," Transportation, Springer, vol. 32(6), pages 649-669, November.
    2. Okutani, Iwao & Stephanedes, Yorgos J., 1984. "Dynamic prediction of traffic volume through Kalman filtering theory," Transportation Research Part B: Methodological, Elsevier, vol. 18(1), pages 1-11, February.
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    Keywords

    self-learning; traffic forecasting;

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