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

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

Transportation Research Part E: Logistics and Transportation Review, 2025, vol. 198, issue C

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.

Keywords: High-speed railway; Train travel time; Train delays; Spatial-temporal differences; Kernel density estimation; Markov chain Monte Carlo method (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.tre.2025.104102

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