Integrated Resilience Framework for Urban Highway Networks under Climate Extremes

Thesis Abstract

This study addresses the increasing vulnerability of urban highway networks to climate extremes—heat, flooding, and intense rainfall—and the consequent disruptions to mobility, economy, and public safety. The problem lies in the limited integration of resilience concepts into network-level planning, design, and operation under multi-hazard conditions, which undermines timely recovery and adaptive capacity. The aim is to develop an Integrated Resilience Framework (IRF) that synthesizes predictive, structural, and operational dimensions to enhance the robustness, redundancy, and recovery of urban highway networks amidst climate variability. Specific objectives include (1) delineating a conceptual model that links climate hazard drivers, infrastructural performance, and service level outcomes; (2) identifying critical resilience indicators and thresholds for asset performance, traffic continuity, and critical link redundancy; (3) developing and calibrating a composite resilience index that integrates physical vulnerability, network topology, and adaptive management strategies; (4) formulating decision-support tools that guide prioritized investments, maintenance scheduling, and operational policies under uncertainty; and (5) validating the framework through a multi-hazard case study in a metropolitan corridor with historical climate-risk exposure. The methodology adopts a mixed-methods research design combining quantitative network simulation, statistical modeling, and qualitative stakeholder insights. The population comprises urban highway networks within a metropolitan region characterized by documented climate extremes. A network sample of 1500 road segments is analyzed, with data drawn from municipal GIS layers, traffic counts, incident logs, pavement condition indices, drainage capacity maps, and historical climate records for the past two decades. Instrumentation includes remotely sensed data, field survey checklists, and structured interviews with transportation engineers, city planners, and emergency responders. Validity and reliability are ensured through triangulation, test-retest procedures for survey instruments, and inter-rater reliability checks for qualitative coding. Data analysis proceeds in three strands (i) probabilistic hazard modeling using copula-based rainfall and temperature projections to simulate extreme events and their spatial-temporal impact on network performance; (ii) network performance and resilience assessment using agent-based traffic simulations and finite element–based structural models to quantify service loss, downtime, and recovery trajectories; and (iii) integration via a Bayesian hierarchical framework to update resilience indices as new data accrue. A multi-criteria decision analysis (MCDA) approach, incorporating the Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), informs prioritization of interventions. The theoretical underpinning draws on the Adaptive Systems Theory and the Concepts of Urban Resilience, complemented by the Theory of Planned Resilience Governance, with empirical model specification including a resilience index equation and a structural equation model linking hazard exposure, asset condition, network redundancy, and service level outcomes. Key expected findings include (a) quantified relationships between flood depth, heat-induced pavement deformation, and network service levels; (b) identification of bottleneck corridors whose failure disproportionately escalates system-wide disruption; (c) a transferable resilience index capturing physical, functional, and organizational dimensions; and (d) decision-support tools that yield optimized intervention portfolios under different climate scenarios and budget constraints. The study anticipates demonstrating that incorporating redundancy, proactive maintenance, and real-time operational adjustments significantly reduces expected downtime and recovery time by 20–35% under modeled extremes. Contributions to knowledge comprise a validated IRF that integrates hazard, infrastructure, and governance dimensions into a single decision-support framework, a novel resilience index for urban road networks, and methodological innovations in coupling network simulation with Bayesian updating and MCDA for climate-resilient urban planning. The main conclusion posits that a parsimonious yet comprehensive IRF can guide cities toward cost-effective resilience investments without compromising current service levels. Recommendations emphasize proactive drainage enhancement, pavement rehabilitation aligned with floodplain zoning, data-sharing protocols among agencies, and the establishment of resilience governance committees to institutionalize adaptive management in the face of evolving climate risks.