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Statistical investigation on train primary delay based on real records: evidence from Wuhan–Guangzhou HSR

Author

Listed:
  • Chao Wen
  • Zhongcan Li
  • Javad Lessan
  • Liping Fu
  • Ping Huang
  • Chaozhe Jiang

Abstract

The focus of this study was to conduct statistical analysis on primary delays in Wuhan–Guangzhou high-speed railway (HSR). The main statistics of primary delays were investigated, including delay causes, delay frequencies, delays’ temporal and spatial occurrences, affected number of trains, and delay recovery patterns. Models that can illustrate the primary delays duration and the number of affected trains were developed. Namely, the log-normal and the Weibull distributions are tested, and the results affirm that the former one can better approximate the duration of primary delays. Subsequently, a non-linear regression model to fit the distribution of the affected number of trains was presented. The temporal and spatial analysis of primary delays and capacity utilization show that there is a high degree of dependency between the periods with high delay frequency and capacity bottlenecks. Specifically, wherever there is a high capacity utilization rate, there is a high probability of delay occurrence. This study provides insightful findings that help in understanding the primary delays in HSR operation and conducting further research.

Suggested Citation

  • Chao Wen & Zhongcan Li & Javad Lessan & Liping Fu & Ping Huang & Chaozhe Jiang, 2017. "Statistical investigation on train primary delay based on real records: evidence from Wuhan–Guangzhou HSR," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 5(3), pages 170-189, July.
    • Handle: RePEc:taf:tjrtxx:v:5:y:2017:i:3:p:170-189
    DOI: 10.1080/23248378.2017.1307144
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    References listed on IDEAS

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    1. A. Higgins & E. Kozan, 1998. "Modeling Train Delays in Urban Networks," Transportation Science, INFORMS, vol. 32(4), pages 346-357, November.
    2. Meester, Ludolf E. & Muns, Sander, 2007. "Stochastic delay propagation in railway networks and phase-type distributions," Transportation Research Part B: Methodological, Elsevier, vol. 41(2), pages 218-230, February.
    3. Briggs, Keith & Beck, Christian, 2007. "Modelling train delays with q-exponential functions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(2), pages 498-504.
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    Cited by:

    1. Zhongcan Li & Ping Huang & Chao Wen & Yixiong Tang & Xi Jiang, 2020. "Predictive models for influence of primary delays using high‐speed train operation records," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1198-1212, December.
    2. Liu Yang & Keping Li & Dan Zhao & Shuang Gu & Dongyang Yan, 2019. "A Network Method for Identifying the Root Cause of High-Speed Rail Faults Based on Text Data," Energies, MDPI, vol. 12(10), pages 1-17, May.
    3. Huang, Ping & Wen, Chao & Fu, Liping & Lessan, Javad & Jiang, Chaozhe & Peng, Qiyuan & Xu, Xinyue, 2020. "Modeling train operation as sequences: A study of delay prediction with operation and weather data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    4. Huang, Ping & Guo, Jingwei & Liu, Shu & Corman, Francesco, 2024. "Explainable train delay propagation: A graph attention network approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).

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