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