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Spatiotemporal Traffic-Flow Dependency and Short-Term Traffic Forecasting

Author

Listed:
  • Yang Yue

    (Centre of Urban Planning and Environmental Management, The University of Hong Kong, Pokfulam Road, Hong Kong SAR)

  • Anthony Gar-On Yeh

    (Centre of Urban Planning and Environmental Management, The University of Hong Kong, Pokfulam Road, Hong Kong SAR)

Abstract

Short-term traffic forecasting is playing an increasing role in modern transport management. Although many short-term traffic forecasting methods have been explored, the spatiotemporal dependency of traffic flow, an important characteristic of traffic dynamics that can benefit the forecasting of traffic changes, is often neglected in short-term traffic forecasting. This paper first investigates the spatiotemporal dependency of traffic flow using cross-correlation analysis and then discusses its implications in terms of traffic forecastability and real-time data effectiveness. This can help us to understand traffic flow, and hence improve the performance of forecasting models.

Suggested Citation

  • Yang Yue & Anthony Gar-On Yeh, 2008. "Spatiotemporal Traffic-Flow Dependency and Short-Term Traffic Forecasting," Environment and Planning B, , vol. 35(5), pages 762-771, October.
    • Handle: RePEc:sae:envirb:v:35:y:2008:i:5:p:762-771
    DOI: 10.1068/b33090
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    References listed on IDEAS

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    1. Dia, Hussein, 2001. "An object-oriented neural network approach to short-term traffic forecasting," European Journal of Operational Research, Elsevier, vol. 131(2), pages 253-261, June.
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    Cited by:

    1. Zhang, Liye & Meng, Qiang & Fang Fwa, Tien, 2019. "Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 287-304.

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