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Open AccessArticle

Pre-Disaster Retrofit Decisions for Sustainable Transportation Systems in Urban Areas

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School of Traffic and Transportation Engineering, Central South University, No. 22 South Section Shaoshan Road, Changsha 410075, China
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School of Civil Engineering, Central South University, No. 22 South Section Shaoshan Road, Changsha 410075, China
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Author to whom correspondence should be addressed.
Sustainability 2019, 11(15), 4044; https://doi.org/10.3390/su11154044
Received: 24 June 2019 / Revised: 21 July 2019 / Accepted: 25 July 2019 / Published: 26 July 2019
(This article belongs to the Special Issue Urban Disaster Risk Reduction)
A transportation system is an important material base for implementing timely rescue and emergency evacuation after disasters in urban areas. In order to reduce disaster risks and develop sustainable transportation systems, it is important to improve their resilience and ensure their reliability. This paper mainly studies pre-disaster retrofit decisions for sustainable transportation systems in urban areas. As the optimization goal, pre-disaster retrofit costs and post-disaster restoration costs under constraints of post-disaster system connectivity, travel time reliability, and post-disaster link capacity are taken into account to construct a bi-level stochastic programming model. A method based on the simulated annealing algorithm and Frank–Wolfe algorithm is used to solve the problem. The case study shows that the calculation is quick, and the result is reasonable. The study result proves that the method proposed in this paper can provide an effective solution to such problems. View Full-Text
Keywords: sustainable transportation systems; pre-disaster retrofit; disaster risk reduction; bi-level stochastic programming model; simulated annealing algorithm sustainable transportation systems; pre-disaster retrofit; disaster risk reduction; bi-level stochastic programming model; simulated annealing algorithm
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MDPI and ACS Style

Zhou, Y.; Jiang, Q.; Qin, J. Pre-Disaster Retrofit Decisions for Sustainable Transportation Systems in Urban Areas. Sustainability 2019, 11, 4044.

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