SOCIALLY SUSTAINABLE TRANSPORTATION INFRASTRUCTURE RESILIENCE FOR MAJOR DISASTERS: VULNERABILITY ASSESSMENTS, INTERDEPENDENCY MODELING & NETWORK INTERVENTIONS

Abstract

The critical functionality of infrastructure systems is imperative for the sustenance of societal lifelines and productivity. Nonetheless, these systems are susceptible to external perturbations and subsequent cascading failures. These infrastructures, encompassing social and physical domains, such as communities, transportation networks, and water management systems, do not operate in isolation but as part of a vast and complex connected network. The functional and geographical interdependencies among these systems introduce additional dimensions of risk, vulnerability, and uncertainty, thereby undermining resilience in the face of a changing climate. Despite the growing body of advanced research on the critical interconnectedness of infrastructure systems and intertwined socio-technical systems, there is still a limited grasp on effectively utilizing these intricate relationships to improve operational efficiency in disaster situations, especially under conditions of combined risk events. This research endeavors to explore such interdependencies between critical infrastructure systems, with a particular emphasis on natural disasters, aiming to reinforce community resilience through both engineered and policy-driven interventions.To achieve this objective, the study delineates six primary aims. First, this study explores the interdependencies present in physical infrastructure systems, with a specific focus on transportation and stormwater systems in the context of flash flood vulnerability. It employs a multi-layer network analysis to explain the geographic interdependencies between these systems. The study suggests that the joint vulnerability of such interconnected infrastructure networks can be explained through their topological characteristics integrated with the operational metrics such as daily traffic and water flow rates. Second, the research develops a modeling framework to examine systems’ interdependencies in the context of cascading failures, integrating physical and operational properties with interdependency relationships to monitor spatiotemporal damage propagation. This is achieved through the Susceptible-Exposed-Flooded-Recovered (SEFR) modeling framework in the transportation and water systems, applied during urban flash flooding events, and supported by hydrologic and traffic models to articulate the systems' physical dynamics. Both theoretical and empirical validations affirm the model's broad applicability. Third, the study explores interdependency relationships within socio-physical systems for risk and vulnerability assessments during compounding disaster events. It examines risk communication within communities, highlighting the challenges posed by rapid and diverse communication in social networks, alongside the limited mobilization capacity and operational constraints of physical infrastructures. Utilizing extensive datasets from social media concerning the October 2020 ice storm in Oklahoma, the study employs advanced computational techniques to translate social media narratives into quantifiable metrics of infrastructure risk and vulnerability assessments, thereby uncovering the impacts on vulnerable communities and infrastructures in compounding risk events. Fourth, the research focuses on interventions within transportation infrastructure to enhance disaster resilience, specifically through the development of innovative roadway reconfiguration techniques. By integrating multi-criteria decision analysis, machine learning, and network science metrics, this approach enables transportation planners to make informed decisions regarding roadway configurations, thereby improving both efficiency and resilience. Fifth, this study investigates how public perception, and social dynamics influence transportation policies by integrating socio-demographic, economic, and travel behavior data. Data-driven analyses and case studies reveal that community sentiment significantly impacts network performance and policy effectiveness. The findings suggest the need for community-centric transportation planning will ensure socially acceptable solutions to enhance resilience of the transportation system. Sixth, the study introduces a Digital Twin (DT) framework aimed at enhancing resilience within interdependent socio-physical infrastructure systems. This involves modeling community risk perception behaviors related to infrastructure risk and vulnerabilities during multi-hazard events and developing an interface to incorporate such behaviors and physical system components through an agent-based modeling framework. This framework considers population diversity and equity in infrastructure prioritization and offers a holistic tool that empowers decision-makers to optimize responses to both immediate disasters and long-term planning challenges. The contributions of this study are threefold: theoretical, methodological, and practical. Theoretically, it bridges the gap in understanding the spatiotemporal dimensions of infrastructure interdependency by characterizing networks’ topological credentials, uncertainties, and the cascading nature of failures. Methodologically, it introduces novel approaches for translating community crisis narratives into insights for critical infrastructure impact assessment during compounding disasters, as well as for evaluating roadway configurations to enhance operational performance under normal and emergency conditions. Practically, the development of the decision support framework (i.e., the DT) enables the examination of how different policy measures and schemes may affect communities and infrastructures, providing valuable insights for policymakers focused on strengthening the resilience of essential infrastructure systems.