| description abstract | With the development of multimodel transportation system in megacities, the risk assessment of transportation complex hubs plays a significant role in facing various uncertainties within the urban comprehensive transportation system. This study first identified the hazard sources of various subsystems in urban transportation complex hubs based on the Hazard and Operability (HAZOP) method. In terms of four typical risk events, i.e., fire, flood, stampede, and abnormal retention, the corresponding fault tree models were constructed and transformed into Bayesian networks for quantitative analysis. Thus, the posterior probabilities of root nodes in Bayesian networks were obtained and further used by density-based spatial clustering of applications with noise (DBSCAN) clustering to screen out major hazard sources. Next, a risk assessment index system for urban transportation complex hubs was established from a resilience perspective, including first-level indexes of personnel, equipment, environment, and management, with a number of second-level indexes under them. The Grey-DEMATEL and entropy weight methods were introduced to obtain the combined weight for each index, so that a risk assessment model was developed based on a cloud model. Finally, the Xi’an North Railway Station, China was used as an empirical case study. The results indicate that the comprehensive risk of the station is at a relatively low level. In terms of first-level indexes, the risks of personnel, equipment, and environment are at a relatively low level, while the risk of management is at a normal level. The methodological framework and findings of this study may provide benchmark and guidelines for further resilience assessment of urban infrastructure facilities. This study proposed a methodological framework for risk assessment from a resilience perspective, enabling a comprehensive risk assessment of urban transportation complex hubs. Field data (2022.6-2023.3) from the Xi’an North Railway Station, Shaanxi Province, China were collected and used to identify and screen the major hazard sources, on which a resilience-oriented risk assessment index system was established, so that a cloud model was introduced to quantify risks across various aspects of the station. The results indicate that the risk level for personnel, equipment, and environment is relatively low, with the management risk level being normal, and the overall risk of the system being relatively low. This study provides an innovative method for establishing a risk assessment index system for large comprehensive transportation hubs, in which additional appropriate and updated indexes may be introduced according to research objectives, to enhance the pertinence of risk assessment. Furthermore, the methodology may be extended into other transportation facility assessments, serving as a benchmark for future risk assessment in similar contexts and environments, thereby contributing to improving the overall safety and resilience of urban transportation systems. | |
