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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. Practical Applications: 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. [ABSTRACT FROM AUTHOR]

The fuel consumption and greenhouse gas (GHG) emission patterns of taxis are in accordance with the urban structure and daily travel footprints of residents. With taxi trajectory data from the intelligent transportation system in Xi'an, China, this study excludes trajectories from electric taxis to accurately estimate GHG emissions of taxis. A gradient boosting decision tree (GBDT) model is employed to examine the nonlinear influence of the built environment (BE) on the GHG emissions of taxis on weekdays and weekends in various urban areas. The research findings indicate that the GHG emissions of taxis within the research area exhibit peak levels during the time intervals of 7:00–9:00, 12:00–14:00, and 23:00–0:00, with notably higher emission factors on weekends than on weekdays. Moreover, a clear nonlinear association exists between BE elements and GHG emissions, with a distinct impact threshold. In the different urban areas, the factors that influence emissions exhibit spatial and temporal heterogeneity. Metro/bus/taxi stops density, residential density, and road network density are the most influential BE elements impacting GHG emissions. Road network density has both positive and negative influences on the GHG emissions in various urban areas. Increasing the road network density in subcentral urban areas and increasing the mixed degree of urban functions in newly developed urban centers to 1.85 or higher can help reduce GHG emissions. These findings provide valuable insights for reducing emissions in urban transportation and promoting sustainable urban development by adjusting urban functional areas. [ABSTRACT FROM AUTHOR]

Existing studies have limited evidence about the complex nonlinear impact mechanism of road network topology and built environment on bike-sharing systems' greenhouse gas (GHG) emission reduction benefits. To fill this gap, we examine the nonlinear effects of road network topological attributes and built environment elements on the potential GHG emission reduction of dockless bike-sharing (DBS) trips in Shenzhen, China. Various methods are employed in the research framework of this study, including a GHG emission reduction estimation model, spatial design network analysis (sDNA), gradient boosting decision tree (GBDT), and partial dependence plots (PDPs). Results show that road network topological variables have the leading role in determining the potential GHG emission reduction of DBS trips, followed by land use variables and transit-related variables. Moreover, the nonlinear impacts of road network topological variables and built environment variables show certain threshold intervals for the potential GHG emission reduction of DBS trips. Furthermore, the impact of built environment on the potential GHG emission reduction of DBS trips is moderated by road network topological indicators (closeness and betweenness). Compared with betweenness, closeness has a greater moderating effect on built environment variables. These findings provide empirical evidence for guiding bike-sharing system planning, bike-sharing rebalancing strategy optimization, and low-carbon travel policy formulation. [ABSTRACT FROM AUTHOR]