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Peak Traffic Flow Predictions: Exploiting Toll Data from Large Expressway Networks

Author

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  • Ling Shen

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

  • Jian Lu

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

  • Dongdong Geng

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

  • Ling Deng

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

Abstract

Big data from toll stations provides reliable and accurate origin-destination (OD) pair information of expressway networks. However, although the short-term traffic prediction model based on big data is being constantly improved, the volatility and nonlinearity of peak traffic flow restricts the accuracy of the prediction results. Therefore, this research attempts to solve this problem through three contributions, firstly, proposing the use the Pauta criterion from statistics as the standard for defining the anomaly criteria of expressway traffic flows. Through comparison with the common local outlier factor (LOF) method, the rationality and advantages of the Pauta criterion were expounded. Secondly, adding week attributes to data, and splitting the data based on the similarity characteristics of traffic flow time series in order to improve the accuracy and efficiency of data input. Thirdly, by introducing empirical mode decomposition (EMD) to decompose the signal before autoregressive integrated moving average (ARIMA) model training is carried out. The first two contributions are for efficiency, the third is to deal with the volatility and nonlinearity of the abnormal peak training data. Finally, the model is analyzed, based on the expressway toll data of the Jiangsu Province. The results show that the EMD-ARIMA model has more advantages than the ARIMA model when dealing with fluctuating data.

Suggested Citation

  • Ling Shen & Jian Lu & Dongdong Geng & Ling Deng, 2020. "Peak Traffic Flow Predictions: Exploiting Toll Data from Large Expressway Networks," Sustainability, MDPI, vol. 13(1), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2020:i:1:p:260-:d:470338
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    References listed on IDEAS

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    Cited by:

    1. Huang, Zhelin & Xu, Fan & Yang, Fangfang, 2023. "State of health prediction of lithium-ion batteries based on autoregression with exogenous variables model," Energy, Elsevier, vol. 262(PB).
    2. Zhang, Michael PhD & Gao, Hang PhD & Chen, Di & Qi, Yanlin, 2024. "A Data-Driven Approach to Manage High-Occupancy Toll Lanes in California," Institute of Transportation Studies, Working Paper Series qt71d0h6hz, Institute of Transportation Studies, UC Davis.

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