Resilience assessment of High-speed railway networks from the spatio-temporal perspective: A case study in Jiangsu Province, China

Abstract

High-speed railways (HSR) are susceptible to disruptions due to a variety of factors such as extreme weather. Improving the resilience of HSR is crucial for minimizing losses and improving operation efficiency. This paper aims to strengthen the resilience of HSR by reducing network vulnerability and enhancing network reliability. An HSR spatio-temporal network (HSRSN) model is constructed to simulate trains’ operation on railways. The model is grounded in the train timetable, combining infrastructure networks and train operations. Critical trains and critical nodes are components that exhibit reduced resilience when the network is subjected to disruptions. Percolation theory is used to identify the critical trains and the information entropy algorithm is introduced for identifying critical nodes. Additionally, a typhoon occurrence is chosen as the disruption for analyzing network vulnerability and connectivity. As for recovery post-disruptions, a strategy is proposed that utilizes timetable adjustments to mitigate the delays caused by disturbances. The performance of the proposed methods has been demonstrated in the case of the HSR network in Jiangsu Province, China. Results show that suspending critical trains during 13:00–15:00 and 17:00–19:00 would significantly reduce the network’s connectivity. Network vulnerability is correlated with both the information entropy of nodes and the timing of link occurrences.