A resilience optimization model for transportation networks under disasters

Tsai-Yun Liao, Ta-Yin Hu () and Yi-No Ko
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Tsai-Yun Liao: National Chiayi University
Ta-Yin Hu: National Cheng Kung University
Yi-No Ko: National Cheng Kung University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2018, vol. 93, issue 1, No 23, 469-489

Abstract: Abstract Natural and/or man-made disasters have caused serious problems in transportation systems due to their unpredictable and destructive characteristics. Under disasters, transportation infrastructure plays an important role in emergency management; however, this infrastructure is also vulnerable because of disasters. One way to describe the vulnerable is through resilience. Resilience refers to the ability to recover from a disruption under unexpected conditions, such as natural and/or man-made disasters. How to enhance resilience of transportation infrastructure under disasters is an important issue when facing natural or man-made disasters. This study aims to measure and optimize transportation resilience under disasters. An optimization model for resilience under the constraints of budget and traversal time is proposed. One special feature is that preparedness and recovery activities are implicitly considered and incorporated within the optimization model. The mathematical model provides a good connection between preparedness/recovery activities and network-level resilience. In order to illustrate the proposed model, a real city network and assumptions on activities of emergency management are used in a series of numerical experiments. Traffic conditions before and after disasters are evaluated by the simulation-assignment model, DynaTAIWAN. Experiments and results illustrate advantages for network-level transportation resilience assessment and also prioritize preparedness and recovery activities under budget constraints.

Keywords: Resilience; Climate change; Resilience assessment (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (23)

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DOI: 10.1007/s11069-018-3310-3

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