Research on Identification and Analysis of Extreme Behavior Risks in High-Speed Railway Stations’ Key Areas

Abstract

Introduction: High-speed rail stations serve as vital transportation hubs connecting different cities, regions, and even countries. They not only accommodate high-speed trains but also enable seamless transfers with other modes of transportation, facilitating rapid and comfortable travel for passengers. The establishment of high-speed rail stations often coincides with economic development in surrounding areas, attracting investments, businesses, and talents. However, extreme individual behaviors can pose severe destructive impacts on high-speed rail stations and their operations, including violent attacks on passengers and staff, destruction of station facilities, illegal intrusions into restricted areas or tracks, and placement of dangerous items. These behaviors threaten the lives of passengers and staff, disrupt station order, and affect the punctuality and overall efficiency of high-speed trains.

Objectives: This study focuses on the warning methods and technologies for extreme behavior in high-speed rail environments, analyzing the causes, characteristics, and prevention and control methods of extreme behavior. It points out the blind spots in the research of extreme behavior warning for high-speed rail both domestically and internationally, such as the lack of research on the aggregation, classification, and risk assessment techniques of extreme behavior in high-speed rail environments.

Methods: This study uses the R-LVC method to evaluate the extreme behavior risks of high-speed rail systems, taking into account the vulnerability and attractiveness of stations, as well as the direct consequences of extreme behavior. By using expert scoring and AHP method to obtain risk source coefficients, this study proposes a vulnerability assessment method for high-speed railway networks based on station levels to evaluate the impact of extreme behaviors on the high-speed railway system.

Results: The study identifies and assesses extreme behaviors in key areas of high-speed rail stations, calculates risk values, and demonstrates the application of risk assessment through a case study.

Conclusions: The research contributes to the identification and analysis of extreme behavior risks in high-speed rail stations, proposing risk assessment techniques and an early warning model based on the R-LVC method. It highlights the importance of considering station level, vulnerability, attractiveness, and direct consequences of extreme behaviors for comprehensive risk evaluation.