Resilience modeling and pre-hazard mitigation planning of transportation network to support post-earthquake emergency medical response

Yangyang Wu and Suren Chen

Reliability Engineering and System Safety, 2023, vol. 230, issue C

Abstract: Earthquakes can cause traffic network disruption, extensive blacked-out traffic signals, considerable dislocation trips, and numerous injuries. To avoid injury-transporting private vehicles jamming on few routes on a first-come, first-served basis, existence of redundant routes is essential for transporting injured people to hospitals in a timely manner. To save as many lives as possible, a resilient transportation network with redundant paths between injured people and hospitals is crucial following major earthquakes. This study aims to support the emergency medical response through improving system resilience with a focus on redundancy by integrating post-hazard emergency response and pre-hazard mitigation planning holistically. First, a new methodology to quantify the resilience of transportation networks is developed with two main contributions: incorporating infrastructure fragility, seismic uncertainty, search-and-rescue activity, and life vitality decay of injured people to quantify the urgency of hospitalization trips; and introducing equivalent resistance theory to model the redundancy of road networks. Second, the feasibility of developing a resilience-based pre-hazard mitigation strategy of vulnerable infrastructures to proactively support post-earthquake emergency medical response is investigated. A comparison with existing resilience metrics is conducted to reveal the advantage of the proposed methodology. Shelby County, Tennessee, is selected as the prototype community to demonstrate the proposed framework.

Keywords: Emergency medical response; Transportation network; Search and rescue; Earthquake; Resilience (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:230:y:2023:i:c:s0951832022005336

DOI: 10.1016/j.ress.2022.108918

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