Despite their inherent vulnerability to structural and functional degradation, transportation networks play a vital role in the aftermath of disasters by ensuring physical access to the affected communities and providing services according to the generated needs. In this setting of operational conditions and service needs which deviate from normal, a restructuring of network functions is deemed to be beneficial for overall network serviceability. In such context, this paper explores the planning of post-disaster operations on a network following a hazardous event on one of the network’s nodes. Lane reversal, demand regulation and path activation are applied to provide an optimally reconfigured network with reallocated demand, so that the network performance is maximized. The problem is formulated as a bi-level optimization model; the upper level determines the optimal network management strategy implementation scheme while the lower level assigns traffic on the network. Three performance indices are used for that purpose: the total network travel time (TNTT), the total network flow (TNF) and the special origin-destination pair (OD pair) accessibility. A genetic algorithm coupled with a traffic assignment process is used as a solution methodology. Application of the model on a real urban network proves the computational efficiency of the algorithm; the model systematically produces robust results of enhanced network performance, indicating its value as an operation planning tool.
International Bank for Reconstruction and Development / The World Bank. Building resilience. Integrating climate and disaster risk into development. The World Bank Group experience. Washington DC: International Bank for Reconstruction and Development / The World Bank; 2013.
Chen X, Kwan MP, Li Q, Chen J. A model for evacuation risk assessment with consideration of pre- and post-disaster factors. Computers, Environment and Urban Systems. 2012; 36(3): 207–217.
Barrett B, Ran B, Pillai R. Developing a dynamic traffic management modeling framework for hurricane evacuation. Transportation Research Record: Journal of the Transportation Research Board. 2000; 1733: 115–121.
Hamza-Lup GL, Hua KA, Le M, Peng R. Dynamic plan generation and real-time management techniques for traffic evacuation. IEEE Transactions on Intelligent Transportation Systems. 2008; 9(4): 615–624.
Zimmerman C, Brodesky R, Karp J. Routes to effective evacuation planning primer series: Using highways for no-notice evacuations. Report No. FHWA-HOP-08-003. Washington DC: FHWA, U.S. Department of Transportation; 2007.
Menoni S. Chains of damages and failures in a metropolitan environment: Some observations on the Kobe earthquake in 1995. Journal of Hazardous Materials. 2001; 86(1-3): 101–119.
Nojima N. Prioritization in upgrading seismic performance of road network based on system reliability analysis. Paper presented at: The 3rd China-Japan-US Trilateral Symposium on Lifeline Earthquake Engineering; 1998; Kunming, China.
Chang SE, Nojima N. Measuring post-disaster transportation system performance: The 1995 Kobe earthquake in comparative perspective. Transportation Research Part A: Policy and Practice. 2001; 35(6): 475–494.
Chang SE. Transportation planning for disasters: An accessibility approach. Environment and Planning A. 2003; 35(6): 1051–1072.
Ukkusuri SV, Yushimito WF. A methodology to assess the criticality of highway transportation networks. Journal of Transportation Security. 2009; 2(1-2): 29–46.
Iida Y, Kurauchi F, Shimada H. Traffic management system against major earthquakes. IATSS Research. 2000; 24(2): 6–17.
Lin DY, Eluru N, Waller TS, Bhat CR. Evacuation planning using the integrated system of activity-based modeling and dynamic traffic assignment. Transportation Research Record: Journal of the Transportation Research Board. 2009; 2132: 69–77.
Chiu YC, Zheng H, Villalobos J, Gautam B. Modeling no-notice mass evacuation using a dynamic traffic flow optimization model. IIE Transactions. 2007; 39(1): 83–94.
Liu HX, Ban JX, Ma W, Mirchandani PB. Model reference adaptive control framework for real-time traffic management under emergency evacuation. Journal of Urban Planning and Development. 2007; 133(1): 43–50.
Niemeier DA. Accessibility : An evaluation using consumer welfare. Transportation. 1997; 24: 377–396.
Bhat C, Handy S, Kockelman K, Mahmassani H, Chen Q, Weston L. Urban accessibility index: Literature review. Peport No. TX-01/7-4938-1. Austin: Center for Transportation Research, The University of Texas at Austin; 2000.
Geurs KT, Ritsema van Eck JR. Accessibility Measures: Review and Applications. RIVM Report 408505006. Bilthoven: National Institute of Public Health and the Environment; 2001.
Scheurer J, Curtis C. Accessibility measures: Overview and practical applications. Working Paper No. 4: Accessibility measures. Australia: Curtin University of Technology; 2007.
Taylor MAP, Susilawati. Remoteness and accessibility in the vulnerability analysis of regional road networks. Transportation Research Part A: Policy and Practice. 2012; 46(5): 761–771.
Bono F, Gutiérrez E. A network-based analysis of the impact of structural damage on urban accessibility following a disaster: The case of the seismically damaged Port Au Prince and Carrefour urban road networks. Journal of Transport Geography. 2011; 19(6): 1443–1455.
Chen A, Yang C, Kongsomsaksakul S, Lee M. Network-based accessibility measures for vulnerability analysis of degradable transportation networks. Networks and Spatial Economics. 2007; 7(3): 241–256.
Kondo, R., Y. Shiomi, and N. Uno. (2012). Network evaluation based on connectivity reliability and accessibility. Proceedings of the 4th International Symposium on Transportation Network Reliability; 2010 July 22-23; University of Minnesota, US. New York: Springer; 2012.
Sohn J. Evaluating the significance of highway network links under the flood damage: An accessibility approach. Transportation Research Part A: Policy and Practice. 2006; 40(6): 491–506.
Taylor, MAP, Sekhar SVC, D’Este GM. Application of accessibility based methods for vulnerability analysis of strategic road networks. Networks and Spatial Economics. 2006; 6(3-4): 267–291.
Taylor MAP, D’ Este GM. Transport network vulnerability : A method for diagnosis of critical locations in transport infrastructure systems. In: Murray AT, Grubesic TH (editors). Critical infrastructure. Advances in spatial science. Berlin, Heidelberg: Springer, 2007; 9–30.
Sumalee A, Kurauchi F. Network capacity reliability analysis considering traffic regulation after a major disaster. Networks and Spatial Economics. 2006; 6(3-4): 205–219.
Daganzo CF, So SK. Managing evacuation networks. Procedia - Social and Behavioral Sciences. 2011; 17: 405–415.
Sisiopiku VP. Application of traffic simulation modeling for improved emergency preparedness planning. Journal of Urban Planning and Development. 2007; 133(1): 51–60.
Chen M, Chen L, Miller-Hooks E. Traffic signal timing for urban evacuation. Journal of Urban Planning and Development. 2007; 133(1): 30–42.
Dixit VV, Radwan E. Hurricane evacuation: Origin, route, and destination. Journal of Transportation Safety & Security. 2009; 1(1): 74–84.
Bretschneider S, Kimms A. Pattern-based evacuation planning for urban areas. European Journal of Operational Research. 2012; 216(1): 57–69.
Xie C, Turnquist MA. Lane-based evacuation network optimization: An integrated lagrangian relaxation and tabu search approach. Transportation Research Part C: Emerging Technologies. 2011; 19(1): 40-63.
Peeta S, Salman SF, Gunnec D, Viswanath K. Pre-disaster investment decisions for strengthening a highway network. Computers & Operations Research. 2010; 37(10): 1708–1719.
Proos, KA, Steven GP, Querin OM, Xie YM. Multicriterion evolutionary structural optimization using the weighted and the global criterion methods. AIAA Journal. 2001; 39(10): 2006–2012.
Palisade Corporation. Guide to Using Evolver – The Genetic Algorithm Solver for Microsoft Excel. Version 5.0. New York: Palisade Corporation; 2010.