# Improving the Resilience of High-Speed Rail Systems from a Configuration Perspective
## 1 Introduction  
A high-speed rail (HSR) system provides the essential physical basis for regional development [1]. Meanwhile, exposure to various natural and manmade hazards closely relates an HSR system to public safety and economic prosperity. Natural hazards, such as hurricane, earthquakes, flood, etc., can cause high-speed train delays, derailments, overturning, and other accidents. According to a Safety Overview published by the European Union (EU) Agency for Railways, there are 2000 significant accidents on average per year on the railways of the EU Member States, causing about 2000 people to be killed or seriously injured (excluding suicides) each year. In addition, accidents caused by collisions, derailments, and fires in rolling stock have been increasing in recent years [2]. Although there is a low possibility of accidents occurring, such events can be catastrophic due to the size of their impact on society (e.g., economic and human losses) [3]. To make HSR systems work better for society, it is necessary to improve the resilience of HSR systems. HSR system resilience refers to the ability to provide effective services under normal conditions, as well as the ability to absorb external interference and maintain the original functions under uncertain disturbances and restrictive conditions [4, 5]. Compared withother transport systems, HSR systems face three challenges in improving resilience. First, an HSR system should meet the requirements of high speed, high density, and high punctuality. That requires the design, construction, and operation of an HSR system to adhere to extremely high standards to ensure its normal operation and safety. Second, an HSR system involves many organizations, including the operation development department, material management department, transportation coordination and supervision bureau, passenger transport department, dispatching department, etc. [6], and efficient coordination among these organizations is difficult. Third, an HSR system is an integrated and complex system, involving infrastructure, rolling stock, energy, operations, and managerial aspects [7]. Its resilience improvement needs joint action and promotion of multiple parts and components [8]. Previous studies have already focused on the resilience of HSR systems. Many scholars have investigated the causes of railway system failures. For example, [PERSON] and [PERSON] (2024) proved earthquakes have a correlation with railway infrastructure accidents [9]. [PERSON] et al. (2018) revealed the causal relations between primary causes and subsystem failures, resulting in derailment [10]. [PERSON] et al. (2018) also provided an integrated approach of how to deal with the many different risks arising from various sources in railway turnout systems [11]. [PERSON] et al. (2019) presented the effects of rail pad, under sleeper pad, and under ballast mat [12]. Several studies have quantified resilience of rail systems using a data-driven approach [13; 14], topological approach [15; 16], simulation approach [17; 18], or optimization approach [19; 20]. These studies merely evaluate the resilience of a rail network while suffering specific disruptions or disasters, failing to explore the way to improve resilience. Several studies have discussed the driving variable for improving resilience, such as [PERSON] et al. (2018) [21] and [PERSON] et al. (2018) [22]. However, a HSR system is a composite and integrated system, affected by various variables simultaneously. Prior studies have solely focused on a single driving variable or the linear effect of multiple variables, ignoring the complex relationship among multiple driving variables. Hence, the research questions guiding this study are: Which variables are correlated to HSR system resilience? Which variable configurations can be used to improve HSR system resilience. The objectives of this study are to (i) identify the driving variables of resilience improvement for an HSR system and (ii) explore the potential configurations of driving variables.