Multisource data-driven resilience assessment and optimization of metro station public spaces: a case study of Suzhou metro

With the continuous expansion of the underground public space network, metro public spaces are increasingly confronted with challenges like extreme weather events and sudden surges in passenger flow. Existing research lacks a resilience evaluation model based on multisource data, making it difficult to systematically identify weaknesses in composite spaces. Based on multisource data, this paper develops an evaluation model containing three dimensions of resilience, adaptability, and changeability, and employs the TOPSIS-entropy weight-AHP method to standardize the assessment of metro public space data. The Guangji South Road Station on the Suzhou Metro in China serves as a case study to validate the proposed model. Through GIS analysis and a street view semantic segmentation method, the resilience weaknesses of the station in the dimensions of spatial resources, environmental comfort, and management mechanisms are visualized. To bolster resilience, the study advances a strategic framework encompassing spatial transformation, data monitoring, and collaborative management to enhance resilience performance by boosting spatial redundancy, implementing a data-based management mode, and upgrading facility service capabilities. The study confirms that the resilience evaluation model driven by multisource data can identify weak points in metro public space resilience, providing theoretical and practical references for establishing resilient metro public spaces.