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A better understanding of long-range temporal dependence of traffic flow time series

Author

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  • Feng, Shuo
  • Wang, Xingmin
  • Sun, Haowei
  • Zhang, Yi
  • Li, Li

Abstract

Long-range temporal dependence is an important research perspective for modelling of traffic flow time series. Various methods have been proposed to depict the long-range temporal dependence, including autocorrelation function analysis, spectral analysis and fractal analysis. However, few researches have studied the daily temporal dependence (i.e. the similarity between different daily traffic flow time series), which can help us better understand the long-range temporal dependence, such as the origin of crossover phenomenon. Moreover, considering both types of dependence contributes to establishing more accurate model and depicting the properties of traffic flow time series. In this paper, we study the properties of daily temporal dependence by simple average method and Principal Component Analysis (PCA) based method. Meanwhile, we also study the long-range temporal dependence by Detrended Fluctuation Analysis (DFA) and Multifractal Detrended Fluctuation Analysis (MFDFA). The results show that both the daily and long-range temporal dependence exert considerable influence on the traffic flow series. The DFA results reveal that the daily temporal dependence creates crossover phenomenon when estimating the Hurst exponent which depicts the long-range temporal dependence. Furthermore, through the comparison of the DFA test, PCA-based method turns out to be a better method to extract the daily temporal dependence especially when the difference between days is significant.

Suggested Citation

  • Feng, Shuo & Wang, Xingmin & Sun, Haowei & Zhang, Yi & Li, Li, 2018. "A better understanding of long-range temporal dependence of traffic flow time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 639-650.
  • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:639-650
    DOI: 10.1016/j.physa.2017.10.006
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    Citations

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    Cited by:

    1. Ismail Shah & Izhar Muhammad & Sajid Ali & Saira Ahmed & Mohammed M. A. Almazah & A. Y. Al-Rezami, 2022. "Forecasting Day-Ahead Traffic Flow Using Functional Time Series Approach," Mathematics, MDPI, vol. 10(22), pages 1-16, November.
    2. Olivares, Felipe & Sun, Xiaoqian & Wandelt, Sebastian & Zanin, Massimiliano, 2023. "Measuring landing independence and interactions using statistical physics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    3. Yimu Ji & Ye Wu & Dianchao Zhang & Yongge Yuan & Shangdong Liu & Roozbeh Zarei & Jing He, 2020. "A Novel Flash P2P Network Traffic Prediction Algorithm based on ELMD and Garch," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 127-141, March.
    4. Zhou, Hanchu & Chang, Fangrong, 2022. "The long-memory temporal dependence of traffic crash fatality for different types of road users," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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