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<p>During the development of urban subway systems, catastrophic accidents occasionally occur worldwide, exposing the inherent vulnerabilities of these systems. Although such incidents are relatively infrequent, the underground setting and confined spatial characteristics of subway systems can result in significant damage, amplifying the adverse consequences. Given the severity of these outcomes, efforts must be directed toward preventing the loss of human and financial resources by identifying and mitigating the factors contributing to these accidents. To this end, this study employs a hybrid fuzzy-TOPSIS method with ordinal-descriptive transformation—a multi-criteria decision-making tool—to pinpoint the most critical causes of each accident. The novelty of this approach lies in its ability to rank accident causes with high accuracy and practical applicability, particularly due to its capacity to handle mixed data (numerical and descriptive), making it an effective modeling technique. Findings indicate that equipment-related factors, such as defects in the electrical systems of Lighting and Power Substation (LPS) (proximity coeeficeint:0.87) and Rectifier Switch (RS) (proximity coeeficeint:0.87) and technical issues in train roof resistors (DC type train)—noted for their high proximity coefficients—outweigh human and management factors in significance. Within human factors, parameters like train driver fatigue, carelessness, distraction, and non-compliance with safety regulations and instructions emerged as the most critical. Consequently, implementing robust repair and maintenance strategies to enhance equipment reliability, alongside continuous and effective training for personnel, is vital for bolstering subway system safety.</p>

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Identification and prioritization of factors contributing to operational incidents in urban rail systems through risk assessment

  • 期刊论文

  • 发表日期：

    2026年5月22日

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  • Maryam Khadivar,
  • Kamran Rahimov,
  • Ali Naderan,
  • Hassan Javanshir

摘要

During the development of urban subway systems, catastrophic accidents occasionally occur worldwide, exposing the inherent vulnerabilities of these systems. Although such incidents are relatively infrequent, the underground setting and confined spatial characteristics of subway systems can result in significant damage, amplifying the adverse consequences. Given the severity of these outcomes, efforts must be directed toward preventing the loss of human and financial resources by identifying and mitigating the factors contributing to these accidents. To this end, this study employs a hybrid fuzzy-TOPSIS method with ordinal-descriptive transformation—a multi-criteria decision-making tool—to pinpoint the most critical causes of each accident. The novelty of this approach lies in its ability to rank accident causes with high accuracy and practical applicability, particularly due to its capacity to handle mixed data (numerical and descriptive), making it an effective modeling technique. Findings indicate that equipment-related factors, such as defects in the electrical systems of Lighting and Power Substation (LPS) (proximity coeeficeint:0.87) and Rectifier Switch (RS) (proximity coeeficeint:0.87) and technical issues in train roof resistors (DC type train)—noted for their high proximity coefficients—outweigh human and management factors in significance. Within human factors, parameters like train driver fatigue, carelessness, distraction, and non-compliance with safety regulations and instructions emerged as the most critical. Consequently, implementing robust repair and maintenance strategies to enhance equipment reliability, alongside continuous and effective training for personnel, is vital for bolstering subway system safety.