Pre-Disaster Resilient Road Network Investment Strategy With Uncertainty Quantification

Extreme events, such as disasters and severe road accidents, have frequently happened in recent years, threatening thousands of people's lives. Efficient evacuation after extreme events is of utmost importance, which requires transport authorities to plan for and develop solutions to ensure transport infrastructure resiliency to disasters. In this study, we develop a modeling framework for optimizing road network pre-disaster investment strategy under different disaster damage levels. A bi-level multi-objective optimization model is formulated, in which the upper-level aims to maximize the capacity-based functionality and reliability of the road network, which are measured by the resilience polygon area, while minimizing the total budget costs, and the lower-level is the user equilibrium problem. To efficiently solve the model, the Shapley value is used to select candidate edges and obtain a near-optimal project order. Realistic speed data collected from crowd sources is used to explore the dependency between the vulnerability and new steady state with copula functions. The numerical study finds that the dependency of road capacities does affect measurements of road network reliability. The investment strategy is significantly affected by the characteristics of road edges and disaster damage levels. Critical sections that can significantly improve the overall functionality of the network are identified. This paper provides a theoretical basis for authorities to make pre-disaster road network investments based on different disaster damage levels, road attributes and budgets.