Strengthening Community Resilience by Modeling Transportation and Electric Power Network Interdependencies
🤖 AI Summary
This study addresses cascading failures between power and transportation infrastructure induced by hurricanes, focusing on Miami-Dade County. Method: We develop a spatially explicit, multi-agent coupled model that captures bidirectional interdependencies between the two networks and incorporates household-level resilience behaviors. The model explicitly prioritizes traffic signal restoration—a novel operational focus—and integrates realistic constraints including fuel transport disruption and restricted access for repair crews. It integrates high-resolution infrastructure spatial data, hurricane-scenario-driven simulation, and comparative analysis of recovery strategies. Contribution/Results: Results demonstrate that prioritizing traffic signal restoration significantly reduces system-wide power restoration time. Crucially, improved road accessibility and secured fuel supply are identified as fundamental enablers of accelerated power recovery. The model is empirically validated against Hurricane Irma, exhibiting strong robustness and practical utility for infrastructure resilience planning and policy design.
Application Category
📝 Abstract
This study presents an agent-based model (ABM) developed to simulate the resilience of a community to hurricane-induced infrastructure disruptions, focusing on the interdependencies between electric power and transportation networks. In this ABM approach, agents represent the components of a system, where interactions within a system shape intra-dependency of a system and interactions among systems shape interdependencies. To study household resilience subject to a hurricane, a library of agents has been created including electric power network, transportation network, wind/flooding hazards, and household agents. The ABM is applied over the household and infrastructure data from a community (Zip code 33147) in Miami-Dade County, Florida. Interdependencies between the two networks are modeled in two ways, (i) representing the role of transportation in fuel delivery to power plants and restoration teams' access, (ii) impact of power outage on transportation network components. Restoring traffic signals quickly is crucial as their outage can slow down traffic and increase the chance of crashes. We simulate three restoration strategies: component based, distance based, and traffic lights based restoration. The model is validated against Hurricane Irma data, showing consistent behavior with varying hazard intensities. Scenario analyses explore the impact of restoration strategies, road accessibility, and wind speed intensities on power restoration. Results demonstrate that a traffic lights based restoration strategy efficiently prioritizes signal recovery without delaying household power restoration time. Restoration of power services will be faster if restoration teams do not need to wait due to inaccessible roads and fuel transportation to power plants is not delayed.
Problem
Research questions and friction points this paper is trying to address.
Modeling interdependencies between electric power and transportation networks during hurricanes
Evaluating restoration strategies for infrastructure resilience post-hurricane
Assessing impact of road accessibility and fuel delivery on power restoration
Innovation
Methods, ideas, or system contributions that make the work stand out.
Agent-based model simulates hurricane resilience interdependencies
Models power-transport links via fuel delivery and outage impacts
Traffic light-focused restoration speeds recovery without delays
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