Next Article in Journal
Different Approaches to Artificial Intelligence–Based Predictive Maintenance on an Axle Test Bench with Highly Varying Tests
Previous Article in Journal
Experimental Study on Scour Resistance Performance Enhancement of Chongqing Red Clay
Previous Article in Special Issue
Intelligent Monitoring of Tunnel Fire Smoke Based on Improved YOLOX and Edge Computing
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 15
Issue 10
10.3390/app15105233
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Improving the Resilience of High-Speed Rail Systems from a Configuration Perspective

by
Na Zhang
1,*,
Xiaopeng Deng
2 and
Bingyu Chen
1
1
School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou 310018, China
2
School of Civil Engineering, Southeast University, Nanjing 211189, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(10), 5233; https://doi.org/10.3390/app15105233
Submission received: 2 April 2025 / Revised: 30 April 2025 / Accepted: 6 May 2025 / Published: 8 May 2025
(This article belongs to the Special Issue Advances in Developing Urban Resilience Through Environmental and Earth Sciences)
Browse Figures
Versions Notes

Abstract

:
A high-speed rail (HSR) system is directly related to people’s daily travel and safety. To make HSR systems work better for society, it is necessary to improve their resilience. Based on an in-depth literature review, six HSR system ontology-related variables and four resilience attribute-related variables were identified. Then, a questionnaire was designed and distributed to targeted respondents after a pilot survey, aiming to collect experts’ opinions on these driving variables. Subsequently, multiple regression analysis was conducted to check the relationship between driving variables and HSR system resilience. Finally, a fuzzy set qualitative comparative analysis (fsQCA) was carried out to identify the potential configurations of driving variables from a holistic perspective. The results show that all driving variables are significantly correlated to HSR system resilience. Moreover, seven variable configurations were identified and divided into three categories, i.e., strong HSR system ontology–weak resilience attributes, weak HSR system ontology–strong resilience attributes, and strong HSR system ontology–strong resilience attributes. This paper explores the effect of configuration of the driving variables on HSR system resilience, providing a holistic perspective for system resilience literature. The results can also help HSR system-related stakeholders understand the effect of variable combinations on resilience improvement.

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 with other 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, Maneerat and Rungskunroch (2024) proved earthquakes have a correlation with railway infrastructure accidents [9]. Dindar et al. (2018) revealed the causal relations between primary causes and subsystem failures, resulting in derailment [10]. Dindar 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]. Ngamkhanong 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 Aydin et al. (2018) [21] and Ngamkhanong 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. This study will disclose the driving variables and their configurations for improving HSR system resilience to bridge the knowledge gap from prior studies. In addition, this study will aid practitioners in understanding the driving variables of HSR system resilience improvement from a holistic perspective.
This study is structured as follows: Section 2 presents a literature review of the driving variables of HSR system resilience improvement and the configuration analysis on resilience improvement. Section 3 demonstrates the research method, including data collection, variable measurements, and data analysis tools. Results and discussion are shared in Section 4 and Section 5, respectively. Finally, Section 6 and Section 7 provide recommendations and the conclusion, respectively.

2. Literature Review

2.1. Driving Variables of HSR System Resilience Improvement

Many scholars have turned their attention to issues related to the HSR system. To date, the majority of studies have focused on the application of technology, accessibility, external influence, risk management, and competitive advantage. Several studies have tested the validity of one single variable for improving HSR system resilience. For example, Brandenburger and Naumann (2018) explored the possibility of staff repairing damaged parts through remote manipulation, so as to measure the degree of human contribution to system resilience [23]. Ngamkhanong et al. (2018) reviewed various kinds of sensors and their application in resilience monitoring [22]. Abimbola, Baatiema, and Bigdeli (2019) discussed the influence of decentralization on resilience [24]. Okoh and Haugen (2015) proposed that maintenance can improve the resilience of complex technological and organizational systems [25].
System resilience is closely related to system performance during disturbance [26,27]. Figure 1 shows hypothetical system performance curves during disturbance, HP(t), and during normal function, BP(t). The area between BP(t) and HP(t) can be used to evaluate the level of system resilience loss [28].
System resilience includes multiple attributes. According to the Multidisciplinary Center for Earthquake Engineering Research (MCEER), resilience is composed of four dimensions: robustness, redundancy, resourcefulness, and rapidity [29]. Moench (2009) divided resilience into “soft” and “hard” measures. Specifically, hard resilience is reflected in structures and institutions, determining the possibility of system collapse. Soft resilience refers to the ability of a system to absorb strain and recover after disturbance [30]. Hipel et al. (2011) proposed that a system with survivability, viability, and conviviality can minimize the impact of disasters [31]. Yodo et al. (2017) quantified engineering resilience using the combination of two attributes, i.e., reliability and recoverability [32]. Bešinović (2020) proposed six attributes of system resilience, i.e., vulnerability, response, survivability, recovery, mitigation, and preparedness [4]. Although there are different categories of system resilience attributes, they are all related to system ontology and the connotation of resilience. This study identified the driving variables of HSR system resilience improvement from two aspects, i.e., HSR system ontology-related variables and resilience attribute-related variables.

2.1.1. HSR System Ontology-Related Variables

HSR system ontology-related variables affect the vulnerability of an HSR system. Kawakami (2014) indicated that the installment of a positive train control system can prevent collisions and derailments caused by excessive speed [33]. Bugalia et al. (2019) examined the relationship between organizational characteristics of HSR operators and risks management [34]. Brown (2015) identified several contributors to rail accidents, i.e., (i) rack, roadbed, and structures, (ii) signal and communications, (iii) miscellaneous, (iv) train operation–human factors, and (v) mechanical and electrical [35]. Bugalia et al. (2020) highlighted organizational and institutional factors affecting HSR operation safety [6]. Read et al. (2019) emphasized the importance of a control structure model in safety management [8]. Xue et al. (2020) pointed out three variables influencing the vulnerability of HSR project, i.e., capital, technology, and management [36]. Ekanayake et al. (2020) proved that chain vulnerabilities can affect chain resilience [37]. In accordance with previous studies, this study identified six HSR system ontology-related variables, i.e., technical system, quality control, equipment operation and maintenance, organization structure, organization efficiency, and system operation and maintenance.
Technical system (TS) means the interaction and association of technical institutions, team resources, technical services, and other technical consortia. According to the statistical analysis of 80 railway accidents in the UK by San and Yoon (2013), 44% of railway accidents are directly or indirectly caused by technical failures [38]. In addition, Xue et al. (2020) confirmed that different technical standards affect the vulnerability of HSR system [36].
Quality control (QC) refers to the strict implementation of technical standards and operating standards in construction and operation management to ensure structural safety and engineering quality. Facing the same disaster damage, a high-quality HSR system is more stable and safe in the operation stage [36]. In addition, a poor HSR system may directly lead to accidents at the operation stage [39,40]. One prominent example is the detachment of the Fukuoka-ken tunnel cement damaging the train.
Equipment operation and maintenance (EM) is defined as the installation, replacement, or maintenance of related equipment or parts of the HSR system, as well as the handling of faulty equipment and other operations and maintenance work [41]. According to the Union Internationale des Chemins de fer (UIC), about 17% of 1785 railway accidents that occurred in Europe in 2017 were directly caused by internal causes within the railway system [42].
Organizational structure (OS) is the way by which organizational activities are divided, organized, and coordinated [43]. It involves hierarchy layers, horizontal integration level, centralization of authority, and communication patterns [44]. To increase efficiency and reduce operating costs, time-based manufacturing may optimize their organizational structure [45]. Due to the characteristics of high speed and high density, an effective organization structure is crucial for dispatching and commanding.
Organizational efficiency (OE) means the extent to which an organization fulfills its objectives [46]. Efficiency losses will reduce an organization’s resilience [24]. To ensure people’s daily travel and safety under a specific organizational structure, high organizational efficiency, by reasonably distributing responsibility, power, and benefit, is critical.
System operation and maintenance (SO) refers to a kind of operation and maintenance management for emergency command and rescue. Okoh and Haugen (2015) highlighted the improvement of resilience attributes of maintenance [25]. Adaptive maintenance in an HSR system can reduce operation risks. Also, maintenance records can contribute to identifying problems and proposing solutions [47].

2.1.2. Resilience Attribute-Related Variables

Resilience theory introduces four core variables, i.e., persistence, restorative, adaptability, and transformability, which are the basis for identifying resilience attribute-related variables [5,27]. The four core variables are also similar to variables proposed by Bešinović (2020) [4] (i.e., response, survivability, recovery, mitigation and preparedness). In consideration of the daily inspection and maintenance of HSR systems, this study listed disturbance warning as a resilience attribute-related variable, corresponding to the variable “mitigation and preparedness” mentioned by Bešinović (2020) [4]. Ultimately, the resilience attribute-related variables of HSR systems are presented as disturbance warning (DW), persistence ability (PA), function restoration (FR), and adaptability and transformability (AT).
Disturbance warning mainly refers to the ability to take quick and effective means to give early warning and ensure control of sudden events. Based on regular phenomena or precursors, early warning can avoid the failure, so as to deal with the event effectively [22]. If small deviations cannot be warned of in a timely manner, they may cause serious accidents [48].
Persistence ability refers to the ability to maintain normal function within acceptable ranges when uncertainty conditions occur [49]. It is highly related to technology redundancy and can be regarded as an HSR system’s robustness [4].
Function restoration refers to the capacity to restore the original condition or secondary use condition [4,50]. After being damaged by an external force or internal system, function restoration may last a few hours up to several weeks [51]. Meanwhile, the restoration strategy and the accessibility of rescue teams will influence the total unmet demand within the time interval [52,53].
Adaptability means the capacity to adjust responses to changing conditions. Transformability presents the capacity to transform existing functions into new functions under unusual and unstable conditions [54]. By summarizing accident cases or past experience, HSR system operators can improve their adaptability and transformability [4].
Ultimately, ten driving variables of HSR system resilience improvement have been identified and presented in Table 1, including six HSR system ontology-related variables and four resilience attribute-related variables.

2.2. Configuration Analysis on Resilience Improvement

Configuration analysis is widely used in the fields of politics, historical sociology, supply chain management, and infrastructure planning because it can handle complexity by exploring different pathways that generate the same outcome [59,60]. Based on the holistic theory, the configuration analysis approach considers a case to be a whole composed of cause conditions, and therefore pays attention to the complex causal relationship between the conditions configurations and the outcome [61].
Several studies have conducted configuration analysis on resilience using qualitative comparative analysis (QCA). Cairns et al. (2017) illustrated how QCA can be applied in the discipline of geography through a case study of area-level health resilience [59]. Mishra et al. (2017) used fsQCA to identify the combinations of factors that may serve as necessary/sufficient conditions for resilient recovery outcomes [62]. Herrera et al. (2022) compared structural equation models (SEM) and QCA to analyze the possible influence of worries about COVID-19, resilience, and emotional symptomatology towards life satisfaction [63]. Haddoud et al. (2022) adopted a fuzzy set qualitative comparative analysis (fsQCA) to assess psychological traits that are likely to result in resilient entrepreneurial behavior in tourism [64]. Cowell and Cousins (2022) conducted a qualitative comparative analysis of resilience strategy for United States cities [60]. Torres and Augusto (2021) employed fsQCA to find that firms’ attention to social issues and CEO duality might be associated with a higher degree of firm resilience [65]. Salem et al. (2022) used partial least squares–structural equation modeling (PLS-SEM) and fsQCA to examine the role of eco-label hotel engagement as a pathway to sustainable practices via scouting entrepreneurial resilience [66]. Ma and Liu (2022) used fsQCA to explore corporate social responsibility configurations for project-based organizations in enhancing organizational resilience [67].
However, these studies mainly focus on the resilience of entrepreneurial behavior, health, organizations, and cities. The literature regarding variable configuration of HSR system resilience improvement is relative limited. An HSR system is a composite and integrated system, affected by various variables simultaneously. Solely focusing on one single variable may fail to improve HSR system resilience holistically. This paper explored the improvement of HSR system resilience using a configuration analysis tool, providing a holistic perspective for system resilience literature.

3. Research Method

This study aims to explore ways to improve HSR system resilience from a holistic perspective. To achieve this goal, four steps were conducted, as illustrated in Figure 2. Firstly, an in-depth literature review was carried out to identify driving variables of HSR system resilience improvement. Secondly, a questionnaire (as shown in Appendix A) was designed and distributed to targeted respondents after a pilot survey with five professionals, aiming to collect experts’ opinions on these driving variables. Then, multi regression analysis was carried out to check if there is multicollinearity among the driving variables, as well as correlation between the driving variables and HSR system resilience improvement. Finally, fsQCA was conducted to analyze the driving variables from a configuration perspective.

3.1. Data Collection

A questionnaire survey method was adopted to collect data. Before the large-scale investigation, a pilot survey was carried out with five professionals to validate the survey questions. According to the professionals’ suggestions, several ambiguous concepts were explained in the form of footnotes in the questionnaires. Then, 535 questionnaires were distributed within the sampling framework, which contains practitioners in the HSR industry (e.g., HSR construction engineers, rolling stock maintenance engineers, dispatching system staff, etc.) and scholars interested in HSR operation and resilience theory. The final version questionnaire contained two parts. The first part was the respondent’s basic information (e.g., occupation, work experience, etc.). The second part aimed to score driving variables and the resilience improvement. After two months, 270 questionnaires were obtained, a response rate of 50.46%. Table 2 shows the respondents’ profiles. Among them, more than 70% of respondents have more than 5 years of work experience. In addition, the respondents have a variety of backgrounds, with 102 responses from academia and 168 responses from industry.

3.2. Variables Measurement

All of the variables in this study were measured using a 3-item scale, as represented in Table 3. Specifically, TS was measured based on technology standards, advanced technology, and R&D capabilities [56,68]. QC was reflected in adequate preliminary surveys, quality management system, and trial run [36,40]. EM was measured based on access management, acceptance management, and integrated deployment [41]. Following Ahmady et al. (2016), three items were proposed to measure OS [43]. Based on Bartuševičienė and Šakalytė (2013), OE was measured based on organizational climate, staff redundancy, and the decision system [69]. SO was measured by three items: real-time inspection, health files, and emergency management [25,47]. DW was measured as a warning system to call attention in a timely manner to unstable or unusual conditions [58]. PA was measured as the ability to keep normal function facing disturbance [49]. FR was measured by restoration speed, restoration proposal, and execution [23]. Based on Folke et al. (2010), three items were proposed to measure AT: data mining, lessons learned, and system improvement [54].
Regarding HSR system resilience, it was measured by three questions, i.e., (1) “High resilience can improve HSR system’s safety, reliability and punctuality”; (2) “High resilience can reduce the effect of disturbance”; and (3) “High resilience can contribute HSR system to recovery normal function after damaged”.

3.3. Data Analysis Tools

Statistical Product and Service Solutions (SPSS) version 25 was used to conduct multiple regression analysis, aiming at checking the multicollinearity among driving variables and the linear relationship between driving variables and HSR system resilience improvement. Several parameters should be interpreted in the process of using this analysis tool. First, the correlation coefficient value is reflected between −1 and +1. If the value is 0, it shows that there is no relation between the two groups. If the value is closer to 1 or −1, it shows that they are strongly positively or negatively related, respectively [70]. Second, R2 is usually referred to as measure of goodness of fit. The closer the value of R2 to 1, the better the regression line fits the observation [71]. Third, the standardized slope coefficient represents the degree to which the independent variable affects the dependent variable [72]. Fourth, variance inflation factor (VIF) measures the multicollinearity severity in a multiple linear regression model. If the value is bigger than five, multicollinearity may occur in estimating the beta coefficients [73].
FsQCA version 2.5 was used to identify the potential configurations of driving variables from a holistic perspective. Compared with multi regression analysis, fsQCA focuses on causal recipes, which hold the same result may originate from distinct combinations of causal conditions [74]. To obtain a fuzzy set score, the first step for using fsQCA was variables calibration. The threshold for full membership of the condition (fuzzy score of 0.95), full non-membership (fuzzy score 0.05), and the crossover point (fuzzy score 0.5) were adopted in this study. After variable calibration, the necessary conditions were examined. If a variable’s consistency was bigger than 0.9, it was regarded as a necessary condition [75]. Then, a truth table was established to conduct specific analysis and standard analysis for elucidating variable configurations. In this step, there are four possible conditions for variables in intermediate solutions, i.e., core condition, peripheral condition, unnecessary condition, and negative condition [73]. Core condition means a necessary variable presenting in the solution. Peripheral condition notes an unnecessary variable presenting in the solution. Unnecessary condition indicates that a variable may or may not presents in the solution [61]. Negative condition means a variable is absent in the solution. Solution consistency measures the extent to which the solution is a subset of the result set. Coverage measures how much of the outcome is covered by each of the solutions, and overall by all the solutions [76].

4. Results

4.1. Multiple Regression Analysis

This study correlated the perceptions of driving variables with resilience improvement. Table 4 shows that all Pearson correlations between driving variables and resilience improvement are positive and significant at a level of 1%. The highest correlation is between FR and resilience improvement (0.795) and the lowest is between TS and resilience improvement (0.564).
Under the premise of ten driving variables correlated with resilience improvement, multiple regression analysis was carried out to test the relative importance of the driving variables to resilience improvement. As shown in Table 5, the adjusted R2 is statistically significant at a level of 1%, meaning the model fits well. The variance inflation factor (VIF) value of OS is bigger than five, indicating that problems from multicollinearity may occur in estimating the beta coefficients [73]. Nevertheless, the rest of variables received a VIF value less than five, meaning that they cannot cause multicollinearity issues. To test the relative contribution of each driving variable, standardized slope coefficients were analyzed. Three driving variables are statically significant at a confidence level of 1%, i.e., PA, FR, and AT. Most variables were not significant in the multiple regression analysis, indicating that the relationships between most of driving variables and resilience improvement may be nonlinear. Hence, solely focusing on one single variable and discussing its relative importance is meaningless.
Although all driving variables’ coefficients are small except PA and FR, the result should be interpreted carefully, as all driving variables have high correlations with resilience improvement. The high correlations among driving variables indicate that multicollinearity exists and the small coefficients are meaningless. Hence, it is better to discuss the influence of variable combinations from a configuration perspective.

4.2. FsQCA Analysis

To identify the potential variable combinations, fsQCA was conducted. In dealing with variable selection, all driving variables were analyzed except OS. OS was not selected because it would cause a problem of multicollinearity, according to the regression analysis result.
The necessary condition analysis was carried out, which aims to check if the result is a subset of a certain condition set [74]. Table 6 shows the necessary condition analysis result. All driving variables’ consistency exceed the threshold of 0.75, meaning they have influence on outcome (i.e., resilience improvement) [77,78]. Moreover, the consistency of seven driving variables was bigger than 0.9, indicating these conditions are almost necessary to outcome in all cases [73,74].
Configuration analysis was conducted to disclose the sufficiency analysis of the results caused by various configurations made up of multiple conditions. A truth table was developed to analyze causal recipes. A consistency threshold of 0.9 and a frequency threshold of 1 were adopted to identify configurations related to the outcome [73]. Table 7 shows the intermediate solution for the outcome. The overall consistency received a value of 0.976 and the solution coverage is 0.880, meaning that the conjuncture result covers 88% of the outcome. In addition, the result presents seven configurations for HSR system resilience improvement, i.e., ① TS*~OE, ② ~OE*SO, ③ ~OE*DW, ④ SO*PA*FR*AT, ⑤ ~TS*OE*PA*FR*AT, ⑥ QC*EM*DW*PA*FR*AT, and ⑦ ~TS*QC*EM*SO*DW*PA*FR.

5. Discussion

According to their necessary conditions, the seven configurations are divided into three categories, as shown in Figure 3. The first category is strong HSR system ontology–weak resilience attributes, including configuration 1 and configuration 2. The second category is weak HSR system ontology–strong resilience attributes, including configuration 3 and configuration 5. The third category is strong HSR system ontology–strong resilience attributes, including configuration 4, configuration 6, and configuration 7.

5.1. Strong HSR System Ontology–Weak Resilience Attributes

The category “strong HSR system ontology–weak resilience attributes” recommends two variables to improve HSR system resilience, i.e., TS and SO. Past accidents have proven the importance of the two variables. Typical cases of HSR accidents caused by technology failure and lack of maintenance include: the Japanese Shinkansen accident due to cracked bogies and oil leakage near the gearbox in 2017; the South Korean KTX HSR derailment due to screws falling off in 2011; the TGV train collapsing due to roadbed collapse causing derailment in 1993; and the German ICE884 derailing due to the blind use of a new technology.
Previous studies also propose to improve system ontology vulnerability. An HSR system can be regarded as a complex system composed of numerous interrelated and interconnected elements. Its vulnerability stems from its specific characteristics [79]. Bešinović (2020) pointed out that improving the vulnerable part of infrastructure can enhance its ability to withstand the disturbance [4]. Nelson et al. (2019) highlighted that redirecting the most vulnerable elements to the new elements can deal with future calamities [80]. HSR system operators should strictly follow the technical standards, specifications, and drawings to ensure the reliable of HSR system ontology.

5.2. Weak HSR System Ontology–Strong Resilience Attributes

The category “weak HSR system ontology–strong resilience attributes” includes configuration 3 and configuration 5. Configuration 3 recommends that effective disturbance warning can significantly improve HSR system resilience, even it has a poor organizational efficiency. This is consistent with prior studies. Preparedness is necessary when certain disruption can be predicted [4]. Early warning indicators are crucial to assessing system resilience [81]. Relying on the real-time estimation of expected damage probability and lead time, an early-warning system can be established [1].
Configuration 5 suggests that PA, FR, and AT can improve the resilience in a poor HSR technology system. Excellent persistence ability can contribute to the HSR system maintaining its normal function in unusual conditions [4]. A damaged HSR system with excellent function restoration ability can resume normal work rapidly by allocating internal and external resources [23]. In response to unstable or unusual conditions, HSR operators with superior adaptability and transformability may formulate specific technical solutions to reduce the adverse impact on HSR facilities and their operations.

5.3. Strong HSR System Ontology–Strong Resilience Attributes

The category “strong HSR system ontology–strong resilience attributes” is the most ideal way to improve HSR system resilience. Configuration 4 suggests that effective system operation and maintenance can reduce the requirement of disturbance warning. That may be because direct and indirect system risks can be identified in a timely manner and then eliminated through regular system maintenance [82]. Guo et al. (2020) also highlighted that regular track inspections can ensure rail safety [83]. In addition, health files can help HSR operators track and analyze past operation data, and thus prevent a certain extent of risks.
Configuration 6 indicates perfect resilience attributes, coupled with strict quality control and equipment maintenance, can significantly improve resilience. Configuration 7 shows that the combination of QC, EM, SO, DW, PA, and FR can improve HSR system resilience even with a poor technology system.

6. Recommendation

According to the results, there are three ways to improve HSR system resilience, i.e., enhancing the HSR system ontology, improving system resilience attributes, and mixed strategies.

6.1. Enhancing HSR System Ontology

According to the core conditions, three variables are key to enhance HSR system ontology, i.e., QC, EM, SO. HSR system quality is the first line of defense against the natural disaster and man-made disturbances. HSR system operators can adopt a quality function deployment-based framework to identify critical system properties for reducing risk exposure and effectively eliminating various potential disasters [84]. To ensure the HSR system is of high quality, zero defect acceptance should be a necessary prerequisite [40]. Also, to strengthen the quality control of HSR projects during the construction stage, it is necessary to give priority to the use of advanced and mature technology.
Equipment reliability is highly related to system reliability. The reliability of unit equipment under special circumstances can guarantee the safety and operation capability of higher running speeds and greater train density. HSR system-related stakeholders can increase financial investment to improve facilities and maintain a reliable environment. Meanwhile, maintenance innovation can enhance the efficiency and quality of equipment operation and maintenance [85].
To ensure the health of an HSR system, the specific content of the HSR system should first be clarified. According to UIC, an HSR system includes operation scheduling, infrastructure construction, fixed equipment and mobile equipment, dynamic monitoring, and other subsystems [86]. Based on these components, HSR operators can focus on risk factors that threaten the operational safety of various HSR subsystems. Also, advanced science, technology, and analysis methods can be used for real-time monitoring [27]. Among these, the health record is a useful tool to grasp the health state of an entire HSR system in a timely manner. Furthermore, HSR operators should focus on cross-domain and cross-disciplinary knowledge interaction and experience mining to guarantee overall operation and maintenance [35,87].

6.2. Improving System Resilience Attributes

According to the core conditions, four variables are critical to improve system resilience attributes, i.e., DW, PA, FR, AT. Disturbance warning is useful for avoiding disasters [48,81]. For example, the main reason for the Ningbo-Wenzhou Railway incident is the failure of early warning system. To identify disturbances early, installing a large number of automatic alarm systems for emergencies is a common way. Also, “Internet + HSR” application technology are helpful to achieve the overall safety, efficiency, and rapid response of a system.
To improve persistence ability, maintaining a good level of safety and normal operation is critical. According to the Smart HSR 2035 research project funded by the Chinese Academy of Engineering, decision-making departments should have a high level of knowledge and competence, and strengthen daily emergency decision-making exercises to ensure that they can respond quickly and efficiently prevent the spread of disasters.
To guarantee function restoration, several aspects can be considered. First, a backup line can be prepared. When a line is damaged, an HSR should still have a certain transportation capacity through the backup line [16]. Second, HSR operators should develop different emergency plans for different sections of each HSR line. Combining the local natural environment and cultural conditions, HSR operators can formulate plans for possible disruptions, and strictly implement various emergency drills in peacetime [88]. Third, HSR operators can set up a special emergency team for possible disruptions. This can efficiently avoid the impact of human factors on the operation of the HSR, and ensure that each incident can be quickly responded by a set of separate special plans. Furthermore, HSR operators should improve road accessibility for rescue teams and arrange the optimal location and allocation of relief trains to enhance the resilience level of the rail network [54,89].
In order to improve the adaptability and transformability of an HSR system, it is necessary to study and reflect on resources, management, and other aspects. First, under the premise that the foreseeable risks of each HSR are basically clear, human resources should be strengthened. Second, a database covering operating cases and accident cases should be established to improve the speed of information acceptance and feedback [35]. Third, a benign improvement mechanism should be formed based on consolidated theoretical study, to ensure that there are reasons for the transformation to be followed [5].

6.3. Mixed Strategies

Relying on the conditions’ negation, several configurations are recommended to improve HSR system resilience under certain situations. When the organizational efficiency of an HSR system is low, resilience can be improved through recipes 1–3, i.e., enhancing technology system, system operation and maintenance, and disturbance warning. When the HSR ontology’s technology system is weak, recipes 5 and 6 can be used to improve the resilience. Specifically, if HSR system excels in aspects of quality control, equipment maintenance, and system operation and maintenance, operators can improve the attributes of resilience in disturbance warning, persistence ability, and function restoration. If the HSR system ontology is not outstanding, the persistence ability, function restoration, and adaptability and transformability need to be strengthened. To achieve the synchronous development of HSR system ontology and resilience attributes, HSR system operators can enhance their standardized management and apply “internet + HSR system” to build data integration platform [85].
Figure 4 shows the resilience loss under different conditions. Enhancing the HSR system ontology can effectively reduce the system vulnerability and the probability of being affected by external disturbance, hence delaying the disturbance occurrence from t2 to t2′ and destroying along curve I. Therefore, the same disturbance theoretically causes different functional losses for different HSR systems. As shown in Figure 4, improving system resilience attributes also can reduce the resilience loss by advancing the recovery time after disturbance from t3 to t3′ and restoring the HSR system along curve II.

7. Conclusions

Improving HSR system resilience is important to ensure HSR systems work better for society. This study explored the driving variables of HSR system resilience from a configuration perspective. Through a literature review, six HSR system ontology-related driving variables and four resilience attributes-related variables were identified. Multiple regression analysis was carried out to test the relationships between the driving variables and resilience improvement. The results show that all driving variables are correlated with resilience improvement. However, OS has a multicollinearity problem with other driving variables. Hence, it was removed in the next analysis.
To explore the potential driving variable combinations, fsQCA was conducted. Seven configurations were identified and categorized into three groups, i.e., strong HSR system ontology–weak resilience attributes, weak HSR system ontology–strong resilience attributes, and strong HSR system ontology–strong resilience attributes. Subsequently, several recommendations were proposed to improve the HSR system resilience.
The findings of this study should be interpreted in the context of study limitations. First, this study used questionnaire survey to obtain data, and thus the survey results might be limited by respondent’s experience and knowledge. Second, this study only identified driving variables from the aspect of HSR system ontology and resilience attributes. Future studies can take more dimensions into consideration (e.g., government services, built environment, society, etc.) and study resilience of HSR systems in China versus in other countries with societal differences.
Despite these limitations, this study presents a configuration perspective for exploring the way to improve HSR system resilience. The potential variable combinations are of great significance to both practitioners and scholars. In practice, this study can help HSR-related stakeholders understand the effect of variable combinations on resilience improvement. In particular, it can provide them with targeted recommendations for enhancing HSR system ontology or promoting system resilience attributes to improve HSR system resilience. In terms of theoretical value, this study reveals the driving variables and seven configurations for improving the HSR system resilience. It also provides a holistic perspective for system resilience literature.

Author Contributions

Conceptualization, N.Z.; methodology, N.Z.; software, N.Z.; validation, B.C.; formal analysis, N.Z.; investigation, X.D.; data curation, B.C.; writing—original draft preparation, N.Z.; writing—review and editing, X.D.; visualization, B.C.; supervision, X.D.; funding acquisition, N.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China under Grant number NSFC-72201249 and the Science Foundation of Zhejiang Sci-Tech University under Grant No. 21052319-Y.

Institutional Review Board Statement

This study did not require ethical approval.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A. Questionnaire

Appendix A.1. Description

(1) This table is divided into two parts. Part one: Basic information survey; Part two: Questionnaire on factors influencing the resilience of high-speed rail (HSR) operation systems.
(2) In this project, HSR refers to a newly built railway with a design speed of 250 km per hour or more, or a railway that has been upgraded and renovated (linearized, standardized gauge), with a design speed of at least 200 km per hour.

Appendix A.2. Part One: Basic Information Survey

(1) Designation:
Scholar: □ Professor/Researcher □ Associate professor/Associate researcher □ As-sistant professor/Lecturer □ Other
Practitioner: □ Senior management personnel (including chief engineer) □ Depart-ment manager □ Project manager □ Other
(2) Relevant work experience:
□ Less than 5 years □ 5–10 years □ 11–15 years □ 16–20 years □ More than 20 years

Appendix A.3. Part Two: Questionnaire on Factors Influencing Resilience of HSR Operation System

Please select the degree of impact of each factor on the resilience improvement of the HSR operation system based on your experience and judgment.
This questionnaire uses a five-point scoring system, where the five-point scale means the degree of impact on the resilience of the HSR operation system.
1—Weak; 2—Weak; 3—General; 4—Strong; 5—Strong.
Please tick “√” in the box, which are all single choice questions.
Table A1. Questionnaire
Table A1. Questionnaire
VariablesItems/Indicators12345
TSThere is a complete set of technical standards√√√√√
We will adopt advanced technology√√√√√
We will keep absorbing external technology and innovating new technology to enhance our technology system.√√√√√
QCWe will conduct an adequate preliminary survey in the aspects of geology, hydrology, meteorology, etc.√√√√√
We will establish high quality management system.√√√√☐
Before operation, we will conduct trial run.√√√√√
EMWe will check product certification, quality inspection and other access certificate for HSR equipment.√√√√√
We will implement an acceptance system for HSR system.√√√√√
We will integrate deployment of fixed facilities and mobile equipment.√√√√√
OSWe will separate the execution and supervision departments.√√√√√
The organization structure can keep information smoothly delivery.√√√√√
We will implement organizational changes and process update, aiming to adapt the environment changes.√√√√√
OETeam members trust each other and have a sense of cooperation and teamwork ability.√√√√√
Key team members have a certain degree of redundancy.√√√√√
We will allow team members to make decision in emergencies.√√√√√
SOWe will establish the high-speed rail emergency command and rescue management system.√√√√√
We will develop real-time operation and maintenance management system.√√√√√
We will develop the health files of each HSR line.√√√√√
DWWe will establish early warning management system√√√√√
We will improve the system automation degree.√√√√√
We will set up dynamic detection and monitoring system.√√√√√
PAThe decision-making department has the ability to respond quickly.√√√√√
The executive department has emergency response capabilities.√√√√√
The dispatch system is established and sound.√√√√√
FRWe will have fast restoration speed.√√√√√
We will set up targeted restoration proposal.√√√√√
We will have strong execution.√√√√√
ATWe will establish HSR accident case database and conduct data mining.√√√√√
We will learn lessons by the way of summarizing major accidents and typical accidents.√√√√√
We will improve HSR system based on previous accidents.√√√√√
HSR resilience improvementHigh resilience can improve HSR system’s safety, reliability and punctuality√√√√√
High resilience can reduce the effect of disturbance√√√√√
High resilience can contribute HSR system to recovery normal function after damaged√√√√√

References

  1. Fa Bozzi, S.; Bilotta, E.; Picozzi, M.; Zollo, A. Feasibility study of a loss-driven earthquake early warning and rapid response systems for tunnels of the Italian high-speed railway network. Soil Dyn. Earthq. Eng. 2018, 112, 232–242. [Google Scholar] [CrossRef]
  2. European Union Agency for Railways. Agency, Railway Safety in the European Union: Safety Overview 2017; European Union Agency for Railways: Valenciennes, France, 2017. [Google Scholar]
  3. Abbasnejadfard, M.; Bastami, M.; Borzoo, S. Novel deterministic and probabilistic resilience assessment measures for engineering and infrastructure systems based on the economic impacts. Int. J. Disaster Risk Reduct. 2022, 75, 102956. [Google Scholar] [CrossRef]
  4. Bešinović, N. Resilience in railway transport systems: A literature review and research agenda. Transp. Rev. 2020, 40, 457–478. [Google Scholar] [CrossRef]
  5. Wan, C.; Yang, Z.; Zhang, D.; Yan, X.; Fan, S. Resilience in transportation systems: A systematic review and future directions. Transp. Rev. 2018, 38, 479–498. [Google Scholar] [CrossRef]
  6. Bugalia, N.; Maemura, Y.; Ozawa, K. Organizational and institutional factors affecting high-speed rail safety in Japan. Saf. Sci. 2020, 128, 104762. [Google Scholar] [CrossRef]
  7. Zhang, N.; Hwang, B.; Deng, X.; Ning, Y. Achieving transient competitive advantage in high-speed rail projects bidding: Sustainable development perspective. Sustain. Dev. 2020, 28, 1738–1754. [Google Scholar] [CrossRef]
  8. Read, G.J.; Naweed, A.; Salmon, P.M. Complexity on the rails: A systems-based approach to understanding safety management in rail transport. Reliab. Eng. Syst. Saf. 2019, 188, 352–365. [Google Scholar] [CrossRef]
  9. Maneerat, P.; Rungskunroch, P. Impact of earthquakes on California’s Railways: A comprehensive correlation analysis of magnitude and hypocenter depths with infrastructure accident. Transp. Res. Interdiscip. Perspect. 2024, 24, 101082. [Google Scholar] [CrossRef]
  10. Dindar, S.; Kaewunruen, S.; An, M.; Sussman, J.M. Bayesian Network-based probability analysis of train derailments caused by various extreme weather patterns on railway turnouts. Saf. Sci. 2018, 110, 20–30. [Google Scholar] [CrossRef]
  11. Dindar, S.; Kaewunruen, S.; An, M. Identification of appropriate risk analysis techniques for railway turnout systems. J. Risk Res. 2018, 21, 974–995. [Google Scholar] [CrossRef]
  12. Ngamkhanong, C.; Nascimento, A.T.; Kaewunruen, S. Economics of track resilience, IOP Conference Series: Materials Science and Engineering. IOP Publ. 2019, 471, 062040. [Google Scholar]
  13. Janić, M. Modelling the resilience of rail passenger transport networks affected by large-scale disruptive events: The case of HSR (high speed rail). Transportation 2018, 45, 1101–1137. [Google Scholar] [CrossRef] [PubMed]
  14. Chen, Z.; Wang, Y. Impacts of severe weather events on high-speed rail and aviation delays. Transp. Res. Part D Transp. Environ. 2019, 69, 168–183. [Google Scholar] [CrossRef]
  15. Mattsson, L.-G.; Jenelius, E. Vulnerability and resilience of transport systems–A discussion of recent research. Transp. Res. Part A: Policy Pract. 2015, 81, 16–34. [Google Scholar] [CrossRef]
  16. Lu, Q.-C. Modeling network resilience of rail transit under operational incidents. Transp. Res. Part A Policy Pract. 2018, 117, 227–237. [Google Scholar] [CrossRef]
  17. Yap, M.D.; van Oort, N.; van Nes, R.; van Arem, B. Identification and quantification of link vulnerability in multi-level public transport networks: A passenger perspective. Transportation 2018, 45, 1161–1180. [Google Scholar] [CrossRef]
  18. Jenelius, E.; Cats, O. The value of new public transport links for network robustness and redundancy. Transp. A Transp. Sci. 2015, 11, 819–835. [Google Scholar] [CrossRef]
  19. Bababeik, M.; Khademi, N.; Chen, A.; Nasiri, M.M. Vulnerability analysis of railway networks in case of multi-link blockage. Transp. Res. Procedia 2017, 22, 275–284. [Google Scholar] [CrossRef]
  20. Szymula, C.; Bešinović, N. Passenger-centered vulnerability assessment of railway networks. Transp. Res. Part B Methodol. 2020, 136, 30–61. [Google Scholar] [CrossRef]
  21. Aydin, N.Y.; Duzgun, H.S.; Heinimann, H.R.; Wenzel, F.; Gnyawali, K.R. Framework for improving the resilience and recovery of transportation networks under geohazard risks. Int. J. Disaster Risk Reduct. 2018, 31, 832–843. [Google Scholar] [CrossRef]
  22. Ngamkhanong; Kaewunruen, S.; Costa, B.J.A. State-of-the-Art Review of Railway Track Resilience Monitoring. Infrastructures 2018, 3, 3. [Google Scholar] [CrossRef]
  23. Brandenburger, N.; Naumann, A. Towards remote supervision and recovery of automated railway systems: The staff’s changing contribution to system resilience. In 2018 International Conference on Intelligent Rail Transportation (ICIRT), Singapore, 12–14 December 2018; IEEE: New York, NY, USA, 2018. [Google Scholar]
  24. Abimbola, S.; Baatiema, L.; Bigdeli, M. The impacts of decentralization on health system equity, efficiency and resilience: A realist synthesis of the evidence. Health Policy Plan. 2019, 34, 605–617. [Google Scholar] [CrossRef]
  25. Okoh, P.; Haugen, S. Improving the robustness and resilience properties of maintenance. Process Saf. Environ. Prot. 2015, 94, 212–226. [Google Scholar] [CrossRef]
  26. Walker, B.; Holling, C.S.; Carpenter, S.R.; Kinzig, A. Resilience, adaptability and transformability in social–ecological systems. Ecol. Soc. 2004, 9, 5. [Google Scholar] [CrossRef]
  27. Shafieezadeh, A.; Burden, L.I. Scenario-based resilience assessment framework for critical infrastructure systems: Case study for seismic resilience of seaports. Reliab. Eng. Syst. Saf. 2014, 132, 207–219. [Google Scholar] [CrossRef]
  28. Hosseini, S.; Barker, K.; Ramirez-Marquez, J.E. A review of definitions and measures of system resilience. Reliab. Eng. Syst. Saf. 2016, 145, 47–61. [Google Scholar] [CrossRef]
  29. Bruneau, M.; Chang, S.E.; Eguchi, R.T.; Lee, G.C.; O’Rourke, T.D.; Reinhorn, A.M.; Shinozuka, M.; Tierney, K.; Wallace, W.A.; Von Winterfeldt, D. A framework to quantitatively assess and enhance the seismic resilience of communities. Earthq. Spectra 2003, 19, 733–752. [Google Scholar] [CrossRef]
  30. Moench, M. Adapting to Climate Change and the Risks Associated with Other Natural Hazards: Methods for Moving from Concepts to action; Adaptation to climate change–the Earthscan reader; Earthscan: London, UK, 2009; pp. 249–280. [Google Scholar]
  31. Hipel, K.W.; Kilgour, D.M.; Fang, L. Systems methodologies in vitae systems of systems. J. Nat. Disaster Sci. 2011, 32, 63–77. [Google Scholar] [CrossRef]
  32. Yodo, N.; Wang, P.; Zhou, Z. Predictive resilience analysis of complex systems using dynamic Bayesian networks. IEEE Trans. Reliab. 2017, 66, 761–770. [Google Scholar] [CrossRef]
  33. Kawakami, S. Application of a systems-theoretic approach to risk analysis of high-speed rail project management in the US. Doctoral Dissertation, Massachusetts Institute of Technology, Cambridge, MA, USA, 2014. [Google Scholar]
  34. Bugalia, N.; Maemura, Y.; Ozawa, K. Demand risk management of private High-Speed Rail operators: A review of experiences in Japan and Taiwan. Transp. Policy 2019, 113, 67–76. [Google Scholar] [CrossRef]
  35. Brown, D.E. Text mining the contributors to rail accidents. IEEE Trans. Intell. Transp. Syst. 2015, 17, 346–355. [Google Scholar] [CrossRef]
  36. Xue, Y.; Xiang, P.; Jia, F.; Liu, Z. Risk assessment of high-speed rail projects: A risk coupling model based on system dynamics. Int. J. Environ. Res. Public Health 2020, 17, 5307. [Google Scholar] [CrossRef]
  37. Ekanayake, E.M.A.C.; Shen, G.Q.; Kumaraswamy, M.; Owusu, E.K. Critical supply chain vulnerabilities affecting supply chain resilience of industrialized construction in Hong Kong. Eng. Constr. Archit. Manag. 2020. ahead-of-print. [Google Scholar] [CrossRef]
  38. San Kim, D.; Yoon, W.C. An accident causation model for the railway industry: Application of the model to 80 rail accident investigation reports from the UK. Saf. Sci. 2013, 60, 57–68. [Google Scholar]
  39. Zhang, N.; Deng, X.; Zhao, X.; Chang, T. Exploring the sources of contractors’ competitive advantage on international HSR construction projects. Int. J. Civ. Eng. 2019, 17, 1115–1129. [Google Scholar] [CrossRef]
  40. Wanberg, J.; Harper, C.; Hallowell, M.R.; Rajendran, S. Relationship between Construction Safety and Quality Performance. J. Constr. Eng. Manag. 2013, 139, 0401300310. [Google Scholar] [CrossRef]
  41. He, M.; Li, H.; Duan, Y. Research on railway intelligent operation and maintenance and its system architecture. In Proceedings of the 2019 6th International Conference on Dependable Systems and Their Applications (DSA), Harbin, China, 3–6 January 2020; IEEE: New York, NY, USA, 2020. [Google Scholar]
  42. UIC. Significant accidents occurred in Europe during the year 2017. In UIC Safety Report; UIC: Chicago, IL, USA, 2018; pp. 1–50. [Google Scholar]
  43. Ahmady, G.A.; Mehrpour, M.; Nikooravesh, A. Organizational structure. Procedia-Soc. Behav. Sci. 2016, 230, 455–462. [Google Scholar] [CrossRef]
  44. Germain, R. The role of context and structure in radical and incremental logistics innovation adoption. J. Bus. Res. 1996, 35, 117–127. [Google Scholar] [CrossRef]
  45. Nahm, A.Y.; Vonderembse, M.A.; Koufteros, X.A. The impact of organizational structure on time-based manufacturing and plant performance. J. Oper. Manag. 2003, 21, 281–306. [Google Scholar] [CrossRef]
  46. Denison, D.R.; Haaland, S.; Goelzer, P. Corporate culture and organizational effectiveness: Is Asia different from the rest of the world? Organ. Dyn. 2004, 33, 98–109. [Google Scholar] [CrossRef]
  47. Wan, J.; Tang, S.; Li, D.; Wang, S.; Liu, C.; Abbas, H.; Vasilakos, A.V. A manufacturing big data solution for active preventive maintenance. IEEE Trans. Ind. Inform. 2017, 13, 2039–2047. [Google Scholar] [CrossRef]
  48. Carson, J.; Mannering, F. The effect of ice warning signs on ice-accident frequencies and severities. Accid. Anal. Prev. 2001, 33, 99–109. [Google Scholar] [CrossRef]
  49. Harrison, G.W. Stability under environmental stress: Resistance, resilience, persistence, and variability. Am. Nat. 1979, 113, 659–669. [Google Scholar] [CrossRef]
  50. Werner, E.E. Risk, resilience, and recovery: Perspectives from the Kauai Longitudinal Study. Dev. Psychopathol. 1993, 5, 503–515. [Google Scholar] [CrossRef]
  51. Keiji, K.; Naohiko, H.; Shigeru, M. Simulation analysis of train operation to recover knock-on delay under high-frequency intervals. Case Stud. Transp. Policy 2015, 3, 92–98. [Google Scholar] [CrossRef]
  52. Li, Y.; Zhang, C.; Jia, C.; Li, X.; Zhu, Y. Joint optimization of workforce scheduling and routing for restoring a disrupted critical infrastructure. Reliab. Eng. Syst. Saf. 2019, 191, 106551. [Google Scholar] [CrossRef]
  53. Bababeik, M.; Khademi, N.; Chen, A. Increasing the resilience level of a vulnerable rail network: The strategy of location and allocation of emergency relief trains. Transp. Res. Part E Logist. Transp. Rev. 2018, 119, 110–128. [Google Scholar] [CrossRef]
  54. Folke, C.; Carpenter, S.R.; Walker, B.; Scheffer, M.; Chapin, T.; Rockström, J. Resilience thinking: Integrating resilience, adaptability and transformability. Ecol. Soc. 2010, 15, 20. [Google Scholar] [CrossRef]
  55. Lu, C.; Cai, C. Overview on safety management and maintenance of high-speed railway in China. Transp. Geotech. 2020, 25, 100397. [Google Scholar] [CrossRef]
  56. Hoterova, K.; Dvorak, Z. Comparative analysis of the resilience and vulnerability of the railway infrastructure. MEST J. 2020, 8, 100–106. [Google Scholar] [CrossRef]
  57. Dressler, W.H. Contesting moral capital in the economy of expectations of an extractive frontier. Ann. Am. Assoc. Geogr. 2017, 107, 647–665. [Google Scholar] [CrossRef]
  58. Dakos, V.; Carpenter, S.R.; van Nes, E.H.; Scheffer, M. Resilience indicators: Prospects and limitations for early warnings of regime shifts. Philos. Trans. R. Soc. B: Biol. Sci. 2015, 370, 20130263. [Google Scholar] [CrossRef]
  59. Cairns, J.M.; Wistow, J.; Bambra, C. Making the case for qualitative comparative analysis in geographical research: A case study of health resilience. AREA 2017, 49, 369–376. [Google Scholar] [CrossRef]
  60. Cowell, M.; Cousins, T. Equity and the Chief Resilience Officer in the era of 100 Resilient Cities: A qualitative comparative analysis of US resilience strategies. Cities 2022, 131, 103946. [Google Scholar] [CrossRef]
  61. Rihoux, B.; Ragin, C.C. Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques; Sage Publications: New York, NY, USA, 2008. [Google Scholar]
  62. Mishra, A.; Ghate, R.; Maharjan, A.; Gurung, J.; Pathak, G.; Upraity, A.N. Building ex ante resilience of disaster-exposed mountain communities: Drawing insights from the Nepal earthquake recovery. Int. J. Disaster Risk Reduct. 2017, 22, 167–178. [Google Scholar] [CrossRef]
  63. Herrera, J.S.; Lacomba-Trejo, L.; Valero-Moreno, S.; Montoya-Castilla, I.; Pérez-Marín, M. Do COVID-19 Worries, Resilience and Emotional Distress Influence Life Satisfaction? Outcomes in Adolescents in Ecuador during the Pandemic: SEM vs. QCA. Children 2022, 9, 439. [Google Scholar] [PubMed]
  64. Haddoud, M.Y.; Onjewu, A.-K.E.; Al-Azab, M.R.; Elbaz, A.M. The psychological drivers of entrepreneurial resilience in the tourism sector. J. Bus. Res. 2022, 141, 702–712. [Google Scholar] [CrossRef]
  65. Torres, P.; Augusto, M. Attention to social issues and CEO duality as enablers of resilience to exogenous shocks in the tourism industry. Tour. Manag. 2021, 87, 104400. [Google Scholar] [CrossRef]
  66. Salem, I.E.; Elbaz, A.M.; Al-Alawi, A.; Alkathiri, N.A.; Elkhwesky, Z. Is eco-label hotel engagement the pathway to sustainability practices via entrepreneurial resilience and orientation in Oman? Findings from PLS-SEM and fsQCA. Int. J. Contemp. Hosp. Manag. 2022, 35, 717–742. [Google Scholar] [CrossRef]
  67. Ma, T.; Liu, Y. Multiple paths to enhancing the resilience of project-based organizations from the perspective of CSR configuration: Evidence from the Chinese construction industry. Eng. Constr. Archit. Manag. 2022, 31, 835–865. [Google Scholar] [CrossRef]
  68. Hua, L.; Ying, Z. On a Synergistic Operating Mechanism for China’s Intellectual Property Public Policy System: A Study of China’s Technology Transfer Policy System. Soc. Sci. China 2013, 34, 179–194. [Google Scholar] [CrossRef]
  69. Bartuševičienė, I.; Šakalytė, E. Organizational Assessment: Effectiveness VS. Efficiency. Soc. Transform. Contemp. Soc. 2013, 92, e31–e33. [Google Scholar]
  70. Benesty, J.; Cohen, I.; Huang, Y.; Chen, J. Pearson correlation coefficient. In Noise Reduction in Speech Processing; Springer: Berlin/Heidelberg, Germany, 2009; pp. 1–4. [Google Scholar]
  71. Ohtani, K. Bootstrapping R2 and adjusted R2 in regression analysis. Econ. Model. 2000, 17, 473–483. [Google Scholar] [CrossRef]
  72. Berry, W.D.; Feldman, S.; Stanley Feldman, D. Multiple Regression in Practice; Sage: New York, NY, USA, 1985. [Google Scholar]
  73. Miranda, S.; Tavares, P.; Queiró, R. Perceived service quality and customer satisfaction: A fuzzy set QCA approach in the railway sector. J. Bus. Res. 2018, 89, 371–377. [Google Scholar] [CrossRef]
  74. Ragin, C.C. Redesigning Social Inquiry: Fuzzy Sets and Beyond; University of Chicago Press: Chicago, IL, USA, 2009. [Google Scholar]
  75. Dul, J. Identifying single necessary conditions with NCA and fsQCA. J. Bus. Res. 2016, 69, 1516–1523. [Google Scholar] [CrossRef]
  76. Pappas, N.; Papatheodorou, A. Tourism and the refugee crisis in Greece: Perceptions and decision-making of accommodation providers. Tour. Manag. 2017, 63, 31–41. [Google Scholar] [CrossRef]
  77. Cui, C.; Yi, H. What drives the performance of collaboration networks: A qualitative comparative analysis of local water governance in China. Int. J. Environ. Res. Public Health 2020, 17, 1819. [Google Scholar] [CrossRef]
  78. Navarro, S.; Llinares, C.; Garzon, D. Exploring the relationship between co-creation and satisfaction using QCA. J. Bus. Res. 2016, 69, 1336–1339. [Google Scholar] [CrossRef]
  79. Deng, X.; Pheng, L.S.; Zhao, X. Project System Vulnerability to Political Risks in International Construction Projects: The Case of Chinese Contractors. Proj. Manag. J. 2014, 45, 20–33. [Google Scholar] [CrossRef]
  80. Nelson, A.C.; Stoker, P.; Hibberd, R. Hibberd, Light rail transit and economic recovery: A case of resilience or transformation? Res. Transp. Econ. 2019, 74, 2–9. [Google Scholar] [CrossRef]
  81. Patriarca, R.; Falegnami, A.; De Nicola, A.; Villani, M.; Paltrinieri, N. Serious games for industrial safety: An approach for developing resilience early warning indicators. Saf. Sci. 2019, 118, 316–331. [Google Scholar] [CrossRef]
  82. Selvik, J.T.; Aven, T. A framework for reliability and risk centered maintenance. Reliab. Eng. Syst. Saf. 2011, 96, 324–331. [Google Scholar] [CrossRef]
  83. Guo, F.; Qian, Y.; Wu, Y.; Leng, Z.; Yu, H. Automatic railroad track components inspection using real-time instance segmentation. Comput.-Aided Civ. Infrastruct. Eng. 2020, 36, 362–377. [Google Scholar] [CrossRef]
  84. Mao, Q.; Li, N.; Peña-Mora, F. Quality function deployment-based framework for improving the resilience of critical infrastructure systems. Int. J. Crit. Infrastruct. Prot. 2019, 26, 100304. [Google Scholar] [CrossRef]
  85. Sarkar, D.; Patel, H.; Dave, B. Development of integrated cloud-based Internet of Things (IoT) platform for asset management of elevated metro rail projects. Int. J. Constr. Manag. 2020, 22, 1993–2002. [Google Scholar] [CrossRef]
  86. UIC. What Is High Speed Rail? 2015. Available online: https://uic.org/highspeed#What-is-High-Speed-Rail (accessed on 4 January 2024).
  87. Walker, G.; Strathie, A. Leading indicators of operational risk on the railway: A novel use for underutilised data recordings. Saf. Sci. 2015, 74, 93–101. [Google Scholar] [CrossRef]
  88. Underwood, B.S.; Mascaro, G.; Chester, M.V.; Fraser, A.; Lopez-Cantu, T.; Samaras, C. Past and present design practices and uncertainty in climate projections are challenges for designing infrastructure to future conditions. J. Infrastruct. Syst. 2020, 26, 04020026. [Google Scholar] [CrossRef]
  89. Borghetti, F.; Malavasi, G. Road accessibility model to the rail network in emergency conditions. J. Rail Transp. Plan. Manag. 2016, 6, 237–254. [Google Scholar] [CrossRef]
Applsci 15 05233 g001
Figure 1. System performance pre-disturbance, post-disturbance, and post-restoration (refer to [27]).
Figure 1. System performance pre-disturbance, post-disturbance, and post-restoration (refer to [27]).
Applsci 15 05233 g001
Applsci 15 05233 g002
Figure 2. Research framework.
Figure 2. Research framework.
Applsci 15 05233 g002
Applsci 15 05233 g003
Figure 3. Categories of seven configurations.
Figure 3. Categories of seven configurations.
Applsci 15 05233 g003
Applsci 15 05233 g004
Figure 4. Resilience loss under different conditions.
Figure 4. Resilience loss under different conditions.
Applsci 15 05233 g004
Table 1. Driving variables of resilience improvement of HSR system.
Table 1. Driving variables of resilience improvement of HSR system.
CategoryVariablesSources
HSR system ontology-related variablesTechnology system (TS)[55,56]
Quality control (QC)[36,40]
Equipment operation and maintenance (EM)[41,42]
Organization structure (OS)[43,57]
Organization efficiency (OE)[24,46]
System operation and maintenance (SO)[25,47]
Resilience attribute-related variablesDisturbance warning (DW)[4,58]
Persistence ability (PA)[4,49]
Function restoration (FR)[4,23]
Adaptability and transformability (AT)[4,54]
Table 2. Profiles of respondents.
Table 2. Profiles of respondents.
CategoryCharacteristicFrequency%Total
DesignationScholarProfessor176.30%270
Associate professor3914.44%
Assistant professor/Lecturer248.89%
Other designation in academia228.15%
PractitionerSenior manager124.44%
Department manager4215.56%
Project manager4817.78%
Other designation in industry6624.44%
Years of experience<56323.33%270
5–105219.26%
11–155721.11%
16–205721.11%
>204115.19%
Table 3. Variables measurement.
Table 3. Variables measurement.
VariablesItems/Indicators
Independent variables
TSThere is a complete set of technical standards
We will adopt advanced technology
We will keep absorbing external technology and innovating new technology to enhance our technology system.
QCWe will conduct adequate preliminary surveys in the aspects of geology, hydrology, meteorology, etc.
We will establish high quality management system.
Before operation, we will conduct trial run.
EMWe will check product certification, quality inspection and other access certificate for HSR equipment.
We will implement an acceptance system for HSR system.
We will integrate deployment of fixed facilities and mobile equipment.
OSWe will separate the execution and supervision departments.
The organization structure can keep information smoothly delivery.
We will implement organizational changes and process update, aiming to adapt the environment changes.
OETeam members trust each other and have a sense of cooperation and teamwork ability.
Key team members have a certain degree of redundancy.
We will allow team members to make decision in emergencies.
SOWe will establish the high-speed rail emergency command and rescue management system.
We will develop real-time operation and maintenance management system.
We will develop the health files of each HSR line.
DWWe will establish early warning management system
We will improve the system automation degree.
We will set up dynamic detection and monitoring system.
PAThe decision-making department has the ability to respond quickly.
The executive department has emergency response capabilities.
The dispatch system is established and sound.
FRWe will have fast restoration speed.
We will set up targeted restoration proposal.
We will have strong execution.
ATWe will establish HSR accident case database and conduct data mining.
We will learn lessons by the way of summarizing major accidents and typical accidents.
We will improve HSR system based on previous accidents.
Dependent variable
HSR resilience improvementHigh resilience can improve HSR system’s safety, reliability and punctuality
High resilience can reduce the effect of disturbance
High resilience can contribute HSR system to recovery normal function after damaged
Table 4. Pearson correlation result.
Table 4. Pearson correlation result.
TSQCEMOSOESODWPAFRATRI
TS1.000
QC0.636 **1.000
EM0.616 **0.809 **1.000
OS0.533 **0.737 **0.784 **1.000
OE0.487 **0.611 **0.622 **0.714 **1.000
SO0.434 **0.613 **0.630 **0.682 **0.544 **1.000
DW0.518 **0.771 **0.733 **0.773 **0.618 **0.697 **1.000
PA0.555 **0.763 **0.773 **0.767 **0.688 **0.674 **0.790 **1.000
FR0.524 **0.750 **0.746 **0.814 **0.711 **0.657 **0.791 **0.818 **1.000
AT0.554 **0.715 **0.720 **0.730 **0.653 **0.613 **0.713 **0.753 **0.750 **1.000
RI0.564 **0.754 **0.707 **0.731 **0.643 **0.626 **0.761 **0.791 **0.795 **0.743 **1.000
**. Correlation is significant at the 0.01 level. RI = resilience improvement.
Table 5. Multiple regression analysis result.
Table 5. Multiple regression analysis result.
Driving VariablesStandardized Slope CoefficientsSignificance Level of SlopeCollinearity Statistics
TVIF
TS0.0690.1071.6180.618
QC0.1650.0112.5630.390
EM−0.0660.310−1.018−0.983
OS0.0120.8600.1775.649
OE0.0220.6600.4402.273
SO0.0200.6800.4132.421
DW0.1320.0392.0710.483
PA0.2140.0023.1780.315
FR0.2480.0003.6040.278
AT0.1570.0052.8240.354
F72.348
Adjusted R20.726
Durbin-Watson2.012
Table 6. Necessary conditions.
Table 6. Necessary conditions.
VariablesConsistencyCoverageVariablesConsistencyCoverage
TS (~TS)0.836 (0.865)0.961 (0.572)DW (~DW)0.943 (0.849)0.961 (0.790)
QC (~QC)0.926 (0.867)0.965 (0.749)PA (~PA)0.953 (0.825)0.955 (0.816)
EM (~EM)0.917 (0.852)0.961 (0.721)FR (~FR)0.934 (0.872)0.966 (0.769)
OE (~OE)0.875 (0.891)0.969 (0.645)AT (~AT)0.902 (0.886)0.969 (0.697)
SO (~SO)0.905 (0.809)0.949 (0.683)
Table 7. Intermediate solution.
Table 7. Intermediate solution.
Variables①②③④⑤⑥⑦
TSApplsci 15 05233 i001 ⊗ ⊗
QC ●●
EM ●●
OE⊗⊗⊗ Applsci 15 05233 i001
SO ● ● ●
DW ● ●●
PA ●●●●
FR ●●●●
AT ●●●
Consistency0.9860.9770.9710.9890.9900.9930.994
Raw coverage0.2750.2890.2980.8220.2950.8160.292
Unique coverage0.0010.0010.0070.0250.0010.0290.004
Solution consistency0.976
Solution coverage0.880
Note: Frequency threshold = 1; consistency threshold = 0.90; full circles (●) mean core conditions; hollow circle (Applsci 15 05233 i001) indicates peripheral conditions; barred circles (⊗) indicates a condition’s absence.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhang, N.; Deng, X.; Chen, B. Improving the Resilience of High-Speed Rail Systems from a Configuration Perspective. Appl. Sci. 2025, 15, 5233. https://doi.org/10.3390/app15105233

AMA Style

Zhang N, Deng X, Chen B. Improving the Resilience of High-Speed Rail Systems from a Configuration Perspective. Applied Sciences. 2025; 15(10):5233. https://doi.org/10.3390/app15105233

Chicago/Turabian Style

Zhang, Na, Xiaopeng Deng, and Bingyu Chen. 2025. "Improving the Resilience of High-Speed Rail Systems from a Configuration Perspective" Applied Sciences 15, no. 10: 5233. https://doi.org/10.3390/app15105233

APA Style

Zhang, N., Deng, X., & Chen, B. (2025). Improving the Resilience of High-Speed Rail Systems from a Configuration Perspective. Applied Sciences, 15(10), 5233. https://doi.org/10.3390/app15105233

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop