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Real-Time Traffic Flow Forecasting via a Novel Method Combining Periodic-Trend Decomposition

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  • Wei Zhou

    (School of Transportation, Southeast University, Nanjing 211189, China
    Jiangsu Key Laboratory of Urban ITS, Nanjing 211189, China
    Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, Nanjing 211189, China)

  • Wei Wang

    (School of Transportation, Southeast University, Nanjing 211189, China
    Jiangsu Key Laboratory of Urban ITS, Nanjing 211189, China
    Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, Nanjing 211189, China)

  • Xuedong Hua

    (School of Transportation, Southeast University, Nanjing 211189, China
    Jiangsu Key Laboratory of Urban ITS, Nanjing 211189, China
    Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, Nanjing 211189, China)

  • Yi Zhang

    (School of Transportation, Southeast University, Nanjing 211189, China)

Abstract

Accurate and timely traffic flow forecasting is a critical task of the intelligent transportation system (ITS). The predicted results offer the necessary information to support the decisions of administrators and travelers. To investigate trend and periodic characteristics of traffic flow and develop a more accurate prediction, a novel method combining periodic-trend decomposition (PTD) is proposed in this paper. This hybrid method is based on the principle of “decomposition first and forecasting last”. The well-designed PTD approach can decompose the original traffic flow into three components, including trend, periodicity, and remainder. The periodicity is a strict period function and predicted by cycling, while the trend and remainder are predicted by modelling. To demonstrate the universal applicability of the hybrid method, four prevalent models are separately combined with PTD to establish hybrid models. Traffic volume data are collected from the Minnesota Department of Transportation (Mn/DOT) and used to conduct experiments. Empirical results show that the mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) of hybrid models are averagely reduced by 17%, 17%, and 29% more than individual models, respectively. In addition, the hybrid method is robust for a multi-step prediction. These findings indicate that the proposed method combining PTD is promising for traffic flow forecasting.

Suggested Citation

  • Wei Zhou & Wei Wang & Xuedong Hua & Yi Zhang, 2020. "Real-Time Traffic Flow Forecasting via a Novel Method Combining Periodic-Trend Decomposition," Sustainability, MDPI, vol. 12(15), pages 1-23, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:15:p:5891-:d:387976
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    References listed on IDEAS

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    Cited by:

    1. Shuanfeng Zhao & Chao Wang & Pei Wei & Qingqing Zhao, 2020. "Research on the Deep Recognition of Urban Road Vehicle Flow Based on Deep Learning," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
    2. Xing, Jiping & Wu, Wei & Cheng, Qixiu & Liu, Ronghui, 2022. "Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    3. Huang, Haichao & Chen, Jingya & Sun, Rui & Wang, Shuang, 2022. "Short-term traffic prediction based on time series decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).

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