A cloud diagnostic model for subway emergency response capability

Abstract To enhance the safety operation management of the subway and ascertain the level of the subway department's emergency response capability, a cloud diagnostic model is proposed. This model, grounded in the 4R crisis management theory and the subway's unique characteristics, constructs a comprehensive diagnostic system. The DEMATEL-ANP model is employed to identify the interactive coupling relationship between various indicators in the diagnostic system, and the diagnostic level is judiciously divided. The cloud model is then utilized to holistically diagnose the subway's emergency handling capacity, thereby pinpointing the specific areas for improvement in subway emergency capacity and bolstering the safety and reliability of subway operation. To circumvent the emergence of imaginary numbers in the digital features of the cloud model, the MBCT-SR algorithm is implemented to enhance the cloud model, and the PSCM algorithm is applied to optimize the precision of cloud similarity discrimination. Using the Nanjing Metro Line 2 as a case study, the analysis reveals that the first-level indicators A and B of the object under diagnosis are underperforming. This necessitates a targeted enhancement of both organizational management of subway emergency personnel and capabilities for monitoring and early warning. Ultimately, the comprehensive diagnostic outcome was classified as a ‘good’ level, which is consistent with the actual situation, verifying the effectiveness and practical application value of the diagnostic model.