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Disturbance impact of rainfall on train travel time in China’s high-speed railway network under different spatial–temporal scenarios

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  • Xue, Feng
  • Zeng, Yu
  • Liang, Jielin
  • Ma, Xiaochen
  • Luo, Yongji

Abstract

Severe rainfall often affects train travel times in a high-speed railway network through speed restrictions and facility failures. The disturbance impact reduces the travel experience for passengers and creates enormous difficulties for train reception and departure at stations. This study presents a method for analysing the disturbance impacts of severe rainfall on train travel time in high-speed railway networks, focusing on the heterogeneity of impacts under different spatial–temporal rainfall scenarios. The average delay of trains at each study station under rainfall conditions is obtained by utilising a Markov chain Monte Carlo method based on kernel density estimation. Taking China’s high-speed railway as an example, we calculate the extent of delays and fluctuations in train travel time for different spatial–temporal rainfall scenarios and identify corresponding critical stations, lines, and rainfall periods. The results show that rainfall in the eastern study area has the greatest disturbance impact on train travel time, causing the extent of train delays and travel time fluctuations averaging 17.64 min/1000 km and 7.18 %. Temporally, the impacts caused by rainfall occurring from 6:00 to 9:00 in the eastern study area are more significant, while rainfall from 12:00 to 15:00 has lesser impacts. Meanwhile, when rainfall occurs from 15:00 to 18:00 in the eastern study area, greater attention should be given to the Ningbo station and the Nanjing-Shanghai railway section. The uneven distribution of train flows and their operational characteristics are the main reasons that impacts have spatial–temporal differences. The study conclusions can provide a reference basis for operation managers to adjust train operation schedules under real rainfall scenarios.

Suggested Citation

  • Xue, Feng & Zeng, Yu & Liang, Jielin & Ma, Xiaochen & Luo, Yongji, 2025. "Disturbance impact of rainfall on train travel time in China’s high-speed railway network under different spatial–temporal scenarios," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:transe:v:198:y:2025:i:c:s1366554525001437
    DOI: 10.1016/j.tre.2025.104102
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    1. Hong, Liu & Ouyang, Min & Xu, Min & Hu, Peipei, 2020. "Time-varied accessibility and vulnerability analysis of integrated metro and high-speed rail systems," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    2. Ling, Ximan & Peng, Yang & Sun, Shilin & Li, Panpan & Wang, Pu, 2018. "Uncovering correlation between train delay and train exposure to bad weather," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1152-1159.
    3. Brazil, William & White, Arthur & Nogal, Maria & Caulfield, Brian & O'Connor, Alan & Morton, Craig, 2017. "Weather and rail delays: Analysis of metropolitan rail in Dublin," Journal of Transport Geography, Elsevier, vol. 59(C), pages 69-76.
    4. Li, Baibing, 2019. "Measuring travel time reliability and risk: A nonparametric approach," Transportation Research Part B: Methodological, Elsevier, vol. 130(C), pages 152-171.
    5. Liu, Zhongmei & Zhu, A-Xing & Zhang, Wenxin & Ren, Mei, 2021. "An improved potential-based approach to measuring the daily accessibility of HSR," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 271-284.
    6. Ohis Ilalokhoin & Raghav Pant & Jim W. Hall, 2022. "A multi-track rail model for estimating journey impacts from extreme weather events: a case study of Great Britain’s rail network," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 10(2), pages 133-158, March.
    7. 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).
    8. Zhang, Xu & Zhang, Wei & Lee, Paul Tae-Woo, 2020. "Importance rankings of nodes in the China Railway Express network under the Belt and Road Initiative," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 134-147.
    9. 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.
    10. Lu, Qing-Chang, 2018. "Modeling network resilience of rail transit under operational incidents," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 227-237.
    11. Grazzini, Jakob & Richiardi, Matteo G. & Tsionas, Mike, 2017. "Bayesian estimation of agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 26-47.
    12. Li, Tao & Rong, Lili, 2020. "A comprehensive method for the robustness assessment of high-speed rail network with operation data: A case in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 666-681.
    13. Zhang, Yifan & Ng, S. Thomas, 2022. "Robustness of urban railway networks against the cascading failures induced by the fluctuation of passenger flow," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    14. repec:dau:papers:123456789/3549 is not listed on IDEAS
    15. Chen, Zhenhua & Wang, Yuxuan & Zhou, Lei, 2021. "Predicting weather-induced delays of high-speed rail and aviation in China," Transport Policy, Elsevier, vol. 101(C), pages 1-13.
    16. Tiong, Kah Yong & Ma, Zhenliang & Palmqvist, Carl-William, 2023. "Analyzing factors contributing to real-time train arrival delays using seemingly unrelated regression models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    17. Federico Cavallaro & Francesco Bruzzone & Silvio Nocera, 2023. "Effects of high-speed rail on regional accessibility," Transportation, Springer, vol. 50(5), pages 1685-1721, October.
    18. Vojtěch Nezval & Richard Andrášik & Michal Bíl, 2024. "Impact of storms on rail transport: a case study from Czechia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(4), pages 3189-3212, March.
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