Resilience-Driven Road Network Retrofit Optimization Subject to Tropical Cyclones Induced Roadside Tree Blowdown

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 polynomial-time 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 trilevel algorithm. The random forest method was employed to transform the trilevel algorithm into a single-level 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

References

[1] Akiyama M, Frangopol DM, Ishibashi H. Toward life-cycle reliability-, risk- and resilience-based design and assessment of bridges and bridge networks under independent and interacting hazards: Emphasis on earthquake, tsunami and corrosion. Structure and Infrastructure Engineering, 2020, 16(1): 26-50

[2] Almoghathawi Y, Barker K, Rocco CM, Nicholson CD. A multi-criteria decision analysis approach for importance identification and ranking of network components. Reliability Engineering & System Safety, 2017, 158: 142-151

[3] Budzyński, M., K. Jamroz, and M. Antoniuk. 2017. Effect of the road environment on road safety in Poland. IOP Conference Series: Materials Science and Engineering 245(4). https://doi.org/10.1088/1757-899x/245/4/042065.

[4] Chang L, Peng F, Ouyang Y, Elnashai AS, Spencer BF Jr Bridge seismic retrofit program planning to maximize post earthquake transportation network capacity. Journal of Infrastructure Systems, 2012, 18(2): 75-88

[5] ChienKuo C, Eiki Y, Santoso D. Seismic resilience analysis of a retrofit-required bridge considering moment-based system reliability. Structure and Infrastructure Engineering, 2020

[6] Dong, Y., D.M. Frangopol, and D. Saydam. 2014. Pre-earthquake multi-objective probabilistic retrofit optimization of bridge networks based on sustainability. Journal of Bridge Engineering 19(6): Article 04014018.

[7] Dong Y, Frangopol DM, Sabatino S. Optimizing bridge network retrofit planning based on cost-benefit evaluation and multi-attribute utility associated with sustainability. Earthquake Spectra, 2015, 31(4): 2255-2280

[8] Döyen A, Aras N. An integrated disaster preparedness model for retrofitting and relief item transportation. Networks and Spatial Economics, 2019, 19(4): 1031-1068

[9] Du LL, Peeta S. A stochastic optimization model to reduce expected post-disaster response time through pre-disaster investment decisions. Networks and Spatial Economics, 2014, 14(2): 271-295

[10] Fan Y, Liu C, Lee R, Kiremidjian AS. Highway network retrofit under seismic hazard. Journal of Infrastructure Systems, 2010, 16(3): 181-187

[11] Fawad M, Kalman K, Khushnood RA, Usman M. Retrofitting of damaged reinforced concrete bridge structure. Procedia Structural Integrity, 2019, 18: 189-197

[12] FEMA (Federal Emergency Management Agency). 2003. Multi-hazard loss estimation methodology, hurricane model, HAZUS, technical manual. Washington, DC: Mitigation Division, FEMA, the Department of Homeland Security.

[13] Frank M, Wolfe P. An algorithm for quadratic programming. Naval Research Logistics Quaterly, 1956, 3(1–2): 95-110

[14] Günneç D, Salman FS. Assessing the reliability and the expected performance of a network under disaster risk. OR Spectrum, 2011, 33(3): 499-523

[15] Haikou Municipal Administration Bureau of Haikou City Impact assessment of Typhoon Rammasun on municipal facilities in the central zone of Haikou city and its countermeasures, 2014, Haikou, China: Haikou Municipal Administration Bureau of Haikou City

[16] Hall TM, Jewson S. Statistical modeling of North Atlantic tropical cyclone tracks. Tellus, 2007, 59(4): 486-498

[17] Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: Data mining, inference, and prediction, 2009 2 New York: Springer

[18] Huang Y, Parmelee S, Pang W. Optimal retrofit scheme for highway network under seismic hazards. International Journal of Transportation Science and Technology, 2014, 3(2): 109-128

[19] Jenelius E, Mattsson L-G. Road network vulnerability analysis of area-covering disruptions: A grid-based approach with case study. Transportation Research Part A: Policy and Practice, 2012, 46(5): 746-760.

[20] Kaplan J, Demaria M. A simple empirical model for predicting the decay of tropical cyclone winds after landfall. Journal of Applied Meteorology, 1995, 3411: 2499-2512

[21] Lambert JH, Tsang JL, Thekdi SA. Risk-informed investment for tropical cyclone preparedness of highway signs, signals, and lights. Journal of Infrastructure Systems, 2013, 19(4): 384-394

[22] Latora, V., and M. Marchiori. 2005. Vulnerability and protection of infrastructure networks. Physical Review E 71(1): Article 015103.

[23] Lin, N., K.A. Emanuel, J.A. Smith, and E. Vanmarcke. 2010. Risk assessment of hurricane storm surge for New York City. Journal of Geophysical Research: Atmospheres 115(D18): Article D18121.

[24] Lu J, Gupte A, Huang Y. A mean-risk mixed integer nonlinear program for transportation network protection. European Journal of Operational Research, 2018, 265(1): 277-289

[25] Malmstadt JC, Elsner JB, Jagger TH. Risk of strong hurricane winds to Florida cities. Journal of Applied Meteorology and Climatology, 2010, 49(10): 2121-2132

[26] Miller BI. Characteristics of hurricanes. Science, 1967, 157(3795): 1389-1399

[27] Mitsakis E, Papanikolaou A, Ayfadopoulou G, Salanova J, Doll C, Giannopoulos G, Zerefos C. An integrated framework for linking climate change impacts to emergency adaptation strategies for transport networks. European Transport Research Review, 2014, 6(2): 103-111

[28] Morbin R, Zanini MA, Pellegrino C, Zhang H, Modena C. A probabilistic strategy for seismic assessment and FRP retrofitting of existing bridges. Bulletin of Earthquake Engineering, 2015, 13(8): 2411-2428

[29] National Bureau of Statistics China city statistical yearbook, 2014, Beijing: China Statistics Press

[30] Neale HJ. Highway landscaping influences traffic operation and safety. Traffic Quarterly, 1949, 3(1): 14-22.

[31] Noyan N. Risk-averse two-stage stochastic programming with an application to disaster management. Computers & Operations Research, 2012, 39(3): 541-559

[32] Patriksson M. The traffic assignment problem: Models and methods, 2015, Mineola, NY: Dover Publications

[33] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 2011, 12: 2825-2830.

[34] Peeta S, Salman FS, Gunnec D, Viswanath K. Pre-disaster investment decisions for strengthening a highway network. Computers & Operations Research, 2010, 37(10): 1708-1719

[35] Rockafellar RT, Uryasev SP. Optimization of conditional value-at-risk. Journal of Risk, 2000, 2(3): 21-42

[36] Rokneddin K, Ghosh J, Dueñas-Osorio L, Padgett JE. Bridge retrofit prioritization for ageing transportation networks subject to seismic hazards. Structure and Infrastructure Engineering, 2013, 9(10): 1050-1066

[37] Rumpf J, Weindl H, Höppe P, Rauch E, Schmidt V. Stochastic modelling of tropical cyclone tracks. Mathematical Methods of Operations Research, 2007, 66(3): 475-490

[38] Salmerón J, Apte A. Stochastic optimization for natural disaster asset prepositioning. Production and Operations Management, 2010, 19(5): 561-574

[39] Sarykalin, S., G. Serraino, and S. Uryasev. 2008. Value-at-risk vs. conditional value-at-risk in risk management and optimization. In Tutorials in Operations Research: State-of-the-art decision-making tools in the information-intensive age, ed. Z. Chen, and S. Raghavan, 270–294. Hanover, MD: INFORMS.

[40] Star Map Press. 2014. Hainan Province Map. Map [ca, 1:380,000]. Beijing: Star Map Press.

[41] Wolf KL. Freeway roadside management: The urban forest beyond the white line. Journal of Arboriculture, 2003, 29(3): 127-136.

[42] Wolf, K.L., and N. Bratton. 2006. Urban trees and traffic safety: Considering U.S. roadside policy and crash data. Arboriculture & Urban Forestry 32(4): 170–179.

[43] Ukkusuri SV, Yushimito WF. A methodology to assess the criticality of highway transportation networks. Journal of Transportation Security, 2009, 2(1): 29-46

[44] Venkittaraman A, Banerjee S. Enhancing resilience of highway bridges through seismic retrofit. Earthquake Engineering Structural Dynamics, 2013, 43(8): 1173-1191

[45] Vickery P. Simple empirical models for estimating the increase in the central pressure of tropical cyclones after landfall along the coastline of the United States. Journal of Applied Meteorology and Climatology, 2005, 44(12): 1807-1826

[46] Yang S, Hu F, Jaeger C. Impact factors and risk analysis of tropical cyclones on a highway network. Risk Analysis, 2016, 36(2): 262-277

[47] Yang S, Hu F, Thompson RG, Wang WP, Li Y, Li S, Ni W. Criticality ranking for components of a transportation network at risk from tropical cyclones. International Journal of Disaster Risk Reduction, 2018, 28: 43-55

[48] Ying M, Zhang W, Yu H, Lu X, Feng J, Fan Y, Zhu Y, Chen D. An overview of the China meteorological administration tropical cyclone database. Journal of Atmospheric Oceanic Technology, 2014, 31(2): 287-301

[49] Zanini MA, Faleschini F, Pellegrino C. Cost analysis for maintenance and seismic retrofit of existing bridges. Structure and Infrastructure Engineering, 2016, 12(11): 1411-1427.

[50] Zeigler, A.J. 1986. Guide to management of roadside trees. Report FHWA-IP-86-17. Washington, DC: Federal Highway Administration (FHWA). http://safety.transportation.org/htmlguides/trees/assets/GuideMgmtRoadSideTrees.pdf. Accessed Feb 2006.

[51] Zhou, Y., Q. Jiang, and J. Qin. 2019. Pre-disaster retrofit decisions for sustainable transportation systems in urban areas. Sustainability 11(15): Article 4044.