Unplanned Disruption Analysis in Urban Railway Systems Using Smart Card Data

Abstract Metro system disruptions are a big concern due to their impacts on safety, service quality, and operating efficiency. A better understanding of performance passenger behavior under unplanned is critical for efficient decision making, effective customer communication, identifying potential improvements. However, few studies explore disruption individual behavior, most use manually collected survey data. This study examines the using automated collection data comprehensively analyze impacts. We propose systematic approach evaluate responses in urban railway systems fare (AFC) develop set metrics from perspectives train operations, information provision (communication), bridging strategy (shuttle bus services connect stations impacted by disruption). also an inference method quantify response (e.g. travel or not, change modes) depending trip characteristics with respect location timing disruption. The proposed demonstrated busy metro system. results highlight ability AFC providing new insights analysis disruptions, which difficult extract traditional methods. case shows that network-wide, passengers continue significant amount time after incident ended. highlights importance real-time need timely dissemination.