Due to global change, natural disasters such as floods have become more frequent in recent years. An effective impact assessment of highway networks before and during floods can help transportation departments prioritize resources and take necessary emergency measures. Although current works have assessed the flood impacts from different perspectives, none have comprehensively evaluated the integrated impacts that capture network structural, functional, and social features, limiting their reliability for decision-making and resilience planning in engineering management. To address this gap, we proposed a graph neural network (GNN)-based framework that incorporates two synthesized indicators—the disaster impact index and criticality score—to integrate structural, functional, and social features. These multidimensional features were inputs to the GNN model, enabling it to capture complex interdependencies and more accurately predict traffic flow and speed under disasters. The practicality of this framework was demonstrated in the case study of Harris County affected by floods caused by Hurricane Harvey. The results showed that Beltway 8, IH-10, and IH-45 were most vulnerable to potential impacts before the flood, while Beltway 8, US-59, and IH-10 were most impacted during the flood, highlighting the need for proactive preflood preparedness and prioritized postflood recovery for these critical roadways. The proposed framework captures complex interdependencies among multidimensional features and more accurately predicts traffic flow and speed. Consequently, it provides a more realistic prediction of the uncertainties in transportation network performance under disasters, offering a robust and practical tool for resilience planning and resource prioritization of other critical infrastructure systems in engineering management.
Dong et al. (Fri,) studied this question.