The assessment of the risk associated with urban road networks, particularly in the context of earthquakes, is of paramount importance for the identification and reinforcement of the most vulnerable sections of urban road networks and the selection of optimal emergency rescue routes. This paper proposes an innovative method combining Bayesian networks and multifactor decision theory. It considers the effect of uncertainty brought about by earthquakes on the composition of road networks and distinguishes critical sections that make up road networks under the influence of multiple factors. This enables suggestions to be made for postearthquake emergency rescue work. Seismic hazards can cause structural damage to urban road networks and affect normal access. This paper quantifies the risk of earthquakes to urban road networks and evaluates the seismic capacity of the network by estimating the travel time of each road section and the connectivity of road sections. A Bayesian network model is established, with the pre‐earthquake connectivity of each partial road section defined as the priori probability. The data of the Bayesian network is updated based on the information obtained from the observation of the components and the system. Multiattribute decision theory is employed to calculate the a posteriori probability, which is then related to the travel time and the length of the road sections, as well as the critical parts of the road network and the optimal emergency paths. This paper presents a case study in which the effectiveness of the decision analysis model is verified. The results of this study contribute to the improvement of emergency rescue operations following an earthquake and reinforce critical sections of the road network in advance, thereby enhancing the overall seismic resilience of the road network.
Binyang Xu (Thu,) studied this question.