A novel qualitative risk assessment using the interval-valued spherical fuzzy extension of TOPSIS method: a case study in rail transit systems

Abstract

This article introduces an innovative approach for risk assessment in rail transit systems, addressing the limitations of traditional methodologies. Our proposed method combines the modified Fine–Kinney approach with the interval-valued spherical fuzzy extension of the technique for order of preference by similarity to ideal solution (IVSF-TOPSIS). By leveraging interval-valued fuzzy sets and spherical fuzzy sets, we capture nuanced uncertainties more effectively. This study differs from previous risk assessment methods by integrating IVSF sets into Fine–Kinney methodologies. Unlike traditional methods, our approach integrates IVSF sets to enhance the handling of uncertainty, resulting in a more comprehensive risk assessment. The proposed model includes parameters such as cost, preventability, and personal protective use, alongside severity, probability, and frequency. This inclusion, alongside expert opinions, enriches the analysis and ensures a more realistic risk evaluation. Applied to the Antalya rail transit (ANTRAY) system in Turkey, the study demonstrates the method’s applicability through a comprehensive case study. Four experts with extensive field and academic experience in rail transit systems and risk analysis contributed their evaluations, ensuring the thoroughness and accuracy of the results. Limited experts’ input yields consistent and high-correlation findings, enhancing result validity and applicability. Eight most exposed hazard groups, analyzed in this study, aid in producing applicable solutions for risk mitigation. The outcomes provide a prioritized list of risks and actionable insights for managing these risks effectively. By combining quantitative and qualitative data through intervalvalued fuzzy sets, our approach bridges the gap between different types of information, resulting in a holistic and reliable risk assessment. Consequently, our novel methodology not only overcomes the limitations of traditional approaches but also offers a practical and comprehensive framework for decision making. By providing a clearer understanding of uncertainties and their impacts, our approach contributes to safer and more efficient rail transit operations.