Probabilistic modeling of the risk and resilience of transportation systems for community resilience analysis

This paper proposes a comprehensive probabilistic framework designed to evaluate the seismic resilience of transportation systems. Recognizing the vital role transportation systems play post-earthquake, it is crucial to understand and model their recovery processes. The framework integrates hazard, risk, network, and agent-based models to provide a holistic analysis of transportation system resilience in the face of seismic events. The earthquake event is characterized in terms of ground-shaking and ground-failure intensities, which directly inform risk analysis. The risk models then assess the initial post-earthquake state of the community by quantifying the response and damage of various structures and infrastructure components, the volume of debris, casualties, and fatalities. Next, the analysis proceeds seamlessly with an agent-based simulation of emergency response and recovery operations. Example operations that are modeled comprise search and rescue, injury transport and treatment, hospital functionality restoration, road debris removal, damage inspection, repair mobilization, and the repair of infrastructure components. Uncertainty inherent in these models is propagated using sampling, resulting in the probability distribution of the total community cost. These costs are categorized into direct economic, comprising damage inspection, repair mobilization, and repair of building blocks, bridges and tunnels, and debris removal; indirect economic comprising temporary housing, relocation of businesses, and excess fuel consumption by transportation network users; socioeconomic comprising delay and opportunity losses for users of the transportation network; environmental due to increased air pollution; and direct and indirect social due to fatalities, treating the injured, impacts on the quality of life, and search and rescue. From the probability distribution of the total community cost, a community resilience measure is derived to further inform resilience enhancement strategies, such as bridge retrofit prioritization. The framework is demonstrated using a comprehensive case study that features a virtual city. This detailed representation reveals the type of insights offered by the framework into community resilience, such as the total recovery time, sensitivity of the resilience measure to the earthquake magnitude, probability of catastrophe, share of various cost categories, temporal variation of travel demand and network capacity, and effect of various phenomena on the quantification of community resilience.