Resilience-Driven Road Network Retrofit Optimization Subject to Tropical Cyclones Induced Roadside Tree Blowdown
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ARTICLE
Resilience-Driven Road Network Retrofit Optimization Subject to Tropical Cyclones Induced Roadside Tree Blowdown Fuyu Hu1 • Saini Yang2,3,4,5 • Russell G. Thompson6
Accepted: 5 September 2020 The Author(s) 2020
Abstract This article focuses on decision making for retrofit investment of road networks in order to alleviate severe consequences of roadside tree blowdown during tropical cyclones. The consequences include both the physical damage associated with roadside trees and the functional degradation associated with road networks. A trilevel, two-stage, and multiobjective stochastic mathematical model was developed to dispatch limited resources to retrofit the roadside trees of a road network. In the model, a new metric was designed to evaluate the performance of a road network; resilience was considered from robustness and recovery efficiency of a road network. The proposed model is at least a nondeterministic polynomialtime hardness (NP-hard) problem, which is unlikely to be solved by a polynomial time algorithm. Pareto-optimal solutions for this problem can be obtained by a proposed & Saini Yang [email protected] 1
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
2
Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China
3
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
4
Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs & Ministry of Education, Beijing Normal University, Beijing 100875, China
5
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
6
School of Engineering, The University of Melbourne, Melbourne 3052, Australia
trilevel algorithm. The random forest method was employed to transform the trilevel algorithm into a singlelevel algorithm in order to decrease the computation burden. Roadside tree retrofit of a provincial highway network on Hainan Island, China was selected as a case area because it suffers severely from tropical cyclones every year, where there is an urgency to upgrade roadside trees against tropical cyclones. We found that roadside tree retrofit investment significantly alleviates the expected economic losses of roadside tree blowdown, at the same time that it promotes robustness and expected recovery efficiency of the road network. Keywords Hainan Island Nondominated sorting genetic algorithm II (NSGA II) Random forest method Road network resilience Roadside tree retrofit Tropical cyclones
1 Introduction An increasing concern for the transport sector is that natural disasters can cripple the functionality of a transportation system, especially for road transportation (Mitsakis et al. 2014). Roadside trees are a necessary component in a complete road system.1 Well-distributed roadside trees have many observed benefits. Such trees can enhance the amenity of driving through the
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