Urban transportation system long-term resilience assessment using multi-dimensional dynamic Bayesian network

Abstract

Resilience has increasingly been recognized as crucial for coping with disruptions and sustaining urban transportation systems (UTSs). However, long-term and dynamic resilience research is lacking. Therefore, this study redefines resilience and develops a comprehensive dynamic long-term resilience assessment model for UTSs. To capture the dynamic characteristics of UTS, we constructed a dynamic Bayesian network model to explore the system's latent learning ability. To reflect the multidimensional considerations in measuring system resilience, leading indicators from four dimensions (economic, environmental, social, and technological) are selected. Case studies reveal that 1) UTS resilience shows dynamic characteristics, 2) environmental and technical indicators enhance resilience, 3) learning capability is positively related to resilience, and 4) resilience does not always correlate with economic development or urban GDP. The proposed research framework offers a reference for integrating subjective and objective data, and the evaluation model serves as a guide for dynamic assessments of system resilience.