The rapid development of urban transit systems (UTS) has significantly contributed to economic and social advancement across various sectors [1]. However, the concentration of large crowds within the confined underground spaces of metro networks increases the potential severity of accidents. Such incidents can escalate rapidly, leading to serious consequences [2]. In recent years, failures and accidents have occurred frequently, attributed to diverse causes, including human error, inadequate management, aging infrastructure, and unsafe passenger behavior [3]. These risks underscore the complexity and vulnerability of UTS. The interdependent nature of their components means that errors in any subsystem can trigger widespread failures. Therefore, modeling and assessing system vulnerability is essential for enhancing the safety and reliability of metro operations.