A better understanding of long-range temporal dependence of traffic flow time series
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DOI: 10.1016/j.physa.2017.10.006
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Cited by:
- 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.
- 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).
- 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.
- 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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Keywords
Long-range temporal dependence; Daily temporal dependence; Traffic flow time series;All these keywords.
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