näkemys - Computer Networks - # Modeling Interdependent Infrastructure Systems for Community Resilience

Modeling Interdependencies between Electric Power and Transportation Networks to Enhance Community Resilience against Hurricane Impacts

Q: How can the proposed agent-based modeling framework be extended to incorporate other critical infrastructure systems (e.g., water, communication) and their interdependencies?

The proposed agent-based modeling framework can be extended to incorporate other critical infrastructure systems by introducing additional agents representing water and communication networks. These agents can interact with the existing agents representing electric power and transportation networks to capture the interdependencies among these systems. For water infrastructure, agents can be created to simulate water sources, treatment plants, distribution networks, and wastewater systems. The interactions between the water and power networks, such as the need for electricity to operate water pumps, can be modeled to understand the cascading effects of disruptions. Similarly, communication network agents can be introduced to represent telecommunication towers, cables, and service providers. Interactions between communication and power networks, such as the reliance of communication systems on electricity for operation, can be simulated to assess the impact of disruptions on overall community resilience. By integrating these additional infrastructure systems into the ABM, a more comprehensive understanding of the interdependencies and resilience of the entire community can be achieved.

Q: What are the potential limitations of the current restoration strategies, and how can they be further improved to enhance community resilience?

One potential limitation of the current restoration strategies is the lack of flexibility and adaptability to changing conditions during and after a disaster. The strategies may not account for real-time information on infrastructure damage, resource availability, or evolving community needs, leading to suboptimal restoration outcomes. To address this limitation, the restoration strategies can be enhanced by incorporating dynamic decision-making algorithms that consider real-time data on infrastructure damage, resource allocation, and community priorities. By integrating adaptive decision-making processes into the restoration strategies, emergency managers can respond more effectively to changing conditions and allocate resources efficiently to restore critical services. Another limitation is the focus on individual infrastructure systems rather than a holistic approach to community resilience. To overcome this limitation, restoration strategies can be redesigned to prioritize the restoration of interconnected infrastructure systems simultaneously. By considering the interdependencies among different systems and implementing coordinated restoration efforts, the overall resilience of the community can be enhanced. Additionally, incorporating community feedback and engagement in the restoration process can help identify and address specific needs and vulnerabilities, leading to more targeted and effective restoration strategies.

Q: What are the broader implications of modeling infrastructure interdependencies for disaster risk reduction and sustainable development in hurricane-prone regions?

Modeling infrastructure interdependencies has significant implications for disaster risk reduction and sustainable development in hurricane-prone regions. By understanding how different infrastructure systems are interconnected and how disruptions in one system can impact others, policymakers and emergency managers can develop more resilient and adaptive strategies to mitigate risks and enhance preparedness. One key implication is the identification of critical nodes and vulnerabilities in the infrastructure network, allowing for targeted investments in strengthening these areas to improve overall resilience. By considering interdependencies, decision-makers can prioritize infrastructure upgrades and investments that address multiple systems simultaneously, maximizing the impact of limited resources. Furthermore, modeling infrastructure interdependencies can inform land-use planning and development policies in hurricane-prone regions. By understanding the cascading effects of infrastructure disruptions, urban planners can design more resilient communities that are better equipped to withstand and recover from disasters. This approach can lead to the creation of more sustainable and disaster-resilient infrastructure systems that support long-term development goals while reducing vulnerability to natural hazards.

Keskeiset käsitteet

Interdependencies between electric power and transportation networks significantly impact community resilience to hurricane-induced infrastructure disruptions. Restoration strategies that prioritize the recovery of traffic signals can efficiently restore power services without delaying household power restoration.

Tiivistelmä

The study develops an agent-based model (ABM) to simulate the resilience of a community to hurricane-induced infrastructure disruptions, focusing on the interdependencies between electric power and transportation networks. The ABM includes agents representing hazards, electric power network, transportation network, and households.

Key highlights:

Interdependencies between the electric power and transportation networks are modeled in two ways: (1) the role of transportation in fuel delivery to power plants and restoration teams' access, and (2) the impact of power outage on transportation network components.
Three restoration strategies are simulated: component based, distance based, and traffic lights based restoration.
The 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.
The ABM can be used as a decision-support tool by policymakers and utility/emergency managers to evaluate power outage restoration strategies.

Mukauta tiivistelmää

Kirjoita tekoälyn avulla

Luo viitteet

Käännä lähde

toiselle kielelle

Luo miellekartta

lähdeaineistosta

Siirry lähteeseen

arxiv.org

Tilastot

Restoration time for 75% of households:

Component based: 92 hours (low wind), 375 hours (high wind)
Distance based: 90 hours (low wind), 305 hours (high wind)
Traffic-light based: 74 hours (low wind), 305 hours (high wind)
Transient loss of resilience (TRL/MPR*100%):

Low wind (65 mph): 33.2% (component), 30.4% (distance), 25.8% (traffic-light)
High wind (115 mph): 73.5% (component), 57.4% (distance), 57.0% (traffic-light)

Lainaukset

"Restoring traffic signals quickly is crucial as their outage can slow down traffic and increase the chance of crashes."
"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."

Tärkeimmät oivallukset

Strengthening Community Resilience by Modeling Transportation and Electric Power Network Interdependencies

by Tasnuba Bint... klo arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12978.pdf

Strengthening Community Resilience by Modeling Transportation and Electric Power Network Interdependencies

Syvällisempiä Kysymyksiä

How can the proposed agent-based modeling framework be extended to incorporate other critical infrastructure systems (e.g., water, communication) and their interdependencies?

The proposed agent-based modeling framework can be extended to incorporate other critical infrastructure systems by introducing additional agents representing water and communication networks. These agents can interact with the existing agents representing electric power and transportation networks to capture the interdependencies among these systems. For water infrastructure, agents can be created to simulate water sources, treatment plants, distribution networks, and wastewater systems. The interactions between the water and power networks, such as the need for electricity to operate water pumps, can be modeled to understand the cascading effects of disruptions. Similarly, communication network agents can be introduced to represent telecommunication towers, cables, and service providers. Interactions between communication and power networks, such as the reliance of communication systems on electricity for operation, can be simulated to assess the impact of disruptions on overall community resilience. By integrating these additional infrastructure systems into the ABM, a more comprehensive understanding of the interdependencies and resilience of the entire community can be achieved.

What are the potential limitations of the current restoration strategies, and how can they be further improved to enhance community resilience?

One potential limitation of the current restoration strategies is the lack of flexibility and adaptability to changing conditions during and after a disaster. The strategies may not account for real-time information on infrastructure damage, resource availability, or evolving community needs, leading to suboptimal restoration outcomes. To address this limitation, the restoration strategies can be enhanced by incorporating dynamic decision-making algorithms that consider real-time data on infrastructure damage, resource allocation, and community priorities. By integrating adaptive decision-making processes into the restoration strategies, emergency managers can respond more effectively to changing conditions and allocate resources efficiently to restore critical services.
Another limitation is the focus on individual infrastructure systems rather than a holistic approach to community resilience. To overcome this limitation, restoration strategies can be redesigned to prioritize the restoration of interconnected infrastructure systems simultaneously. By considering the interdependencies among different systems and implementing coordinated restoration efforts, the overall resilience of the community can be enhanced. Additionally, incorporating community feedback and engagement in the restoration process can help identify and address specific needs and vulnerabilities, leading to more targeted and effective restoration strategies.

What are the broader implications of modeling infrastructure interdependencies for disaster risk reduction and sustainable development in hurricane-prone regions?

Modeling infrastructure interdependencies has significant implications for disaster risk reduction and sustainable development in hurricane-prone regions. By understanding how different infrastructure systems are interconnected and how disruptions in one system can impact others, policymakers and emergency managers can develop more resilient and adaptive strategies to mitigate risks and enhance preparedness.
One key implication is the identification of critical nodes and vulnerabilities in the infrastructure network, allowing for targeted investments in strengthening these areas to improve overall resilience. By considering interdependencies, decision-makers can prioritize infrastructure upgrades and investments that address multiple systems simultaneously, maximizing the impact of limited resources.
Furthermore, modeling infrastructure interdependencies can inform land-use planning and development policies in hurricane-prone regions. By understanding the cascading effects of infrastructure disruptions, urban planners can design more resilient communities that are better equipped to withstand and recover from disasters. This approach can lead to the creation of more sustainable and disaster-resilient infrastructure systems that support long-term development goals while reducing vulnerability to natural hazards.