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This paper proposes a hierarchical Bayesian network model (BNM) toquantitatively evaluate the resilience of urban transportation infrastructure.Based on systemic thinkings and sustainability perspectives, we investigate thelong-term resilience of the road transportation systems in four cities of Chinafrom 1998 to 2017, namely Beijing, Tianjin, Shanghai, and Chongqing,respectively. The model takes into account the factors involved in stages ofdesign, construction, operation, management, and innovation of urban roadtransportation, which collected from multi-source data platforms. We test themodel with the forward inference, sensitivity analysis, and backward inference.The result shows that the overall resilience of all four cities' transportationinfrastructure is within a moderate range with values between 50% to 60%.Although they all have an ever-increasing economic level, Beijing and Tianjindemonstrate a clear "V" shape in the long-term transportation resilience, whichindicates a strong multi-dimensional, dynamic, and non-linear characteristic inresilience-economic coupling effect. Additionally, the results obtained fromthe sensitivity analysis and backward inference suggest that urbandecision-makers should pay more attention to the capabilities of quickrebuilding and making changes to cope with future disturbance. As anexploratory study, this study clarifies the concepts of long-termmulti-dimensional resilience and specific hazard-related resilience andprovides an effective decision-support tool for stakeholders when buildingsustainable infrastructure.

Evaluating resilience in urban transportation systems for sustainability: A systems-based Bayesian network model