Relationships Between Evacuation Population Size, Earthquake Emergency Shelter Capacity, and Evacuation Time

Xiujuan Zhao , Wei Xu , Yunjia Ma , Lianije Qin , Junlin Zhang , Ying Wang

International Journal of Disaster Risk Science ›› 2017, Vol. 8 ›› Issue (4) : 457 -470.

PDF
International Journal of Disaster Risk Science ›› 2017, Vol. 8 ›› Issue (4) : 457 -470. DOI: 10.1007/s13753-017-0157-2
Article

Relationships Between Evacuation Population Size, Earthquake Emergency Shelter Capacity, and Evacuation Time

Author information +
History +
PDF

Abstract

Determining the location of earthquake emergency shelters and the allocation of affected population to them are key issues that face shelter planning and emergency management. To solve this emergency shelter location–allocation problem, evacuation time and the construction cost of shelters—both influenced by the evacuation population size and its spatial distribution—are two important considerations. In this article, a mathematical model with two objectives—to minimize total weighted evacuation time (TWET) and total shelter area (TSA)—is allied with a modified particle swarm optimization algorithm to address the problem. The relationships between evacuation population size, evacuation time, and total shelter area are examined using Jinzhan Town in Chaoyang District of Beijing, China, as a case study. The results show that TWET has a power function relationship with TSA under different population size scenarios, and a linear function applies between evacuation population and TWET under different TSAs. The joint relationships of TSA, TWET, and population size show that TWET increases with population increase and TSA decrease, and compared with TSA, population influences TWET more strongly. Given a reliable projection of population change and spatial planning of a study area, this method can be useful for government decision making on the location of earthquake emergency shelters and on the allocation of evacuees to those shelters.

Keywords

China, earthquake shelter location / Evacuation population allocation / Total shelter area / Total weighted evacuation time

Cite this article

Download citation ▾
Xiujuan Zhao, Wei Xu, Yunjia Ma, Lianije Qin, Junlin Zhang, Ying Wang. Relationships Between Evacuation Population Size, Earthquake Emergency Shelter Capacity, and Evacuation Time. International Journal of Disaster Risk Science, 2017, 8(4): 457-470 DOI:10.1007/s13753-017-0157-2

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Alçada-Almeida L, Tralhão L, Santos L, Coutinho-Rodrigues J. A multiobjective approach to locate emergency shelters and identify evacuation routes in urban areas. Geographical Analysis, 2009, 41(1): 9-29

[2]

Barzinpour F, Esmaeili V. A multi-objective relief chain location distribution model for urban disaster management. The International Journal of Advanced Manufacturing Technology, 2014, 70(5–8): 1291-1302

[3]

Bayram V, Tansel , Yaman H. Compromising system and user interests in shelter location and evacuation planning. Transportation Research Part B: Methodological, 2015, 72: 146-163

[4]

Beijing Municipal Institute of City Planning & Design. 2007. Planning of earthquake and emergency shelters in Beijing central districts (outdoors). http://xch.bjghw.gov.cn/web/static/articles/catalog_84/article_7491/7491.html. Accessed 26 Nov 2017 (in Chinese).

[5]

Berman O, Krass D. The generalized maximal covering location problem. Computers & Operations Research, 2002, 29(6): 563-581

[6]

Cai Q, Gong M, Shen B, Ma L, Jiao L. Discrete particle swarm optimization for identifying community structures in signed social networks. Neural Networks, 2014, 58: 4-13

[7]

Chang M, Tseng Y, Chen J. A scenario planning approach for the flood emergency logistics preparation problem under uncertainty. Transportation Research Part E: Logistics and Transportation Review, 2007, 43(6): 737-754

[8]

Church R, Velle CR. The maximal covering location problem. Papers in Regional Science, 1974, 32(1): 101-118

[9]

Dalal J, Mohapatra PK, Mitra GC. Locating cyclone shelters: A case. Disaster Prevention and Management, 2007, 16(2): 235-244

[10]

Dijkstra EW. A note on two problems in connexion with graphs. Numerische Mathematik, 1959, 1(1): 269-271

[11]

Dombroski M, Fischhoff B, Fischbeck P. Predicting emergency evacuation and sheltering behavior: A structured analytical approach. Risk Analysis, 2006, 26(6): 1675-1688

[12]

ESRI (Environmental Systems Research Institute) ArcGIS desktop: Release 10, 2010, Redlands, CA: ESRI

[13]

Fraser SA, Wood NJ, Johnston DM, Leonard GS, Greening PD, Rossetto T. Variable population exposure and distributed travel speeds in least-cost tsunami evacuation modelling. Natural Hazards and Earth System Science, 2014, 14(11): 2975-2991

[14]

Gama M, Santos BF, Scaparra MP. A multi-period shelter location–allocation model with evacuation orders for flood disasters. EURO Journal on Computational Optimization, 2016, 4(3–4): 299-323

[15]

Gates, T.J., D.A. Noyce, A.R. Bill, and N. Van Ee. 2006. Recommended walking speeds for pedestrian clearance timing based on pedestrian characteristics. 85th Annual Meeting of the Transportation Research Board. Paper No. 06-1826, 2006. https://pdfs.semanticscholar.org/fc81/aff9a47546a8034cf0d94438ed7466c97326.pdf. Accessed 14 Nov 2017.

[16]

Ghaderi A, Jabalameli MS, Barzinpour F, Rahmaniani R. An efficient hybrid particle swarm optimization algorithm for solving the uncapacitated continuous location–allocation problem. Networks and Spatial Economics, 2012, 12(3): 421-439

[17]

Hakimi SL. Optimum locations of switching centers and the absolute centers and medians of a graph. Operations Research, 1964, 12(3): 450-459

[18]

Hakimi SL. Optimum distribution of switching centers in a communication network and some related graph theoretic problems. Operations Research, 1965, 13(3): 462-475

[19]

Hu F, Xu W, Li X. A modified particle swarm optimization algorithm for optimal allocation of earthquake emergency shelters. International Journal of Geographical Information Science, 2012, 26(9): 1643-1666

[20]

Hu F, Yang S, Xu W. A non-dominated sorting genetic algorithm for the location and districting planning of earthquake shelters. International Journal of Geographical Informational Science, 2014, 28(7): 1482-1501

[21]

Huang B, Liu N, Chandramouli M. A GIS supported Ant algorithm for the linear feature covering problem with distance constraints. Decision Support Systems, 2006, 42(2): 1063-1075

[22]

Johansson A, Helbing D, Al-Abideen HZ, Al-Bosta S. From crowd dynamics to crowd safety: A video-based analysis. Advances in Complex Systems, 2008, 11(4): 497-527

[23]

Jonkman SN, Maaskant B, Boyd E, Levitan ML. Loss of life caused by the flooding of New Orleans after Hurricane Katrina: Analysis of the relationship between flood characteristics and mortality. Risk Analysis, 2009, 29(5): 676-698

[24]

Kady RA. The development of a movement–density relationship for people going on four in evacuation. Safety Science, 2012, 50(2): 253-258

[25]

Kılcı F, Kara BY, Bozkaya B. Locating temporary shelter areas after an earthquake: A case for Turkey. European Journal of Operational Research, 2015, 243(1): 323-332

[26]

Koks EE, Bočkarjova M, Moel H, Aerts JC. Integrated direct and indirect flood risk modeling: Development and sensitivity analysis. Risk Analysis, 2015, 35(5): 882-900

[27]

Kongsomsaksakul S, Yang C, Chen A. Shelter location–allocation model for flood evacuation planning. Journal of the Eastern Asia Society for Transportation Studies, 2005, 6: 4237-4252.

[28]

Li AC, Nozick L, Xu N, Davidson R. Shelter location and transportation planning under hurricane conditions. Transportation Research Part E: Logistics and Transportation Review, 2012, 48(4): 715-729

[29]

Li X, He J, Liu X. Intelligent GIS for solving high-dimensional site selection problems using ant colony optimization techniques. International Journal of Geographical Informational Science, 2009, 23(4): 399-416

[30]

Liu C, Fan Y, Ordóñez F. A two-stage stochastic programming model for transportation network protection. Computers & Operations Research, 2009, 36(5): 1582-1590

[31]

Marinakis Y, Marinaki M. A particle swarm optimization algorithm with path relinking for the location routing problem. Journal of Mathematical Modelling and Algorithms, 2008, 7(1): 59-78

[32]

Moussaïd M, Helbing D, Theraulaz G. How simple rules determine pedestrian behavior and crowd disasters. Proceedings of the National Academy of Sciences, 2011, 108(17): 6884-6888

[33]

Ng M, Park J, Waller ST. A hybrid bi-level model for the optimal shelter assignment in emergency evacuations. Computer-Aided Civil and Infrastructure Engineering, 2010, 25(8): 547-556

[34]

Pan, A. 2010. The applications of maximal covering model in typhoon emergency shelter location problem. 2010 IEEE international conference on industrial engineering and engineering management, 7–10 December 2010. https://doi.org/10.1109/IEEM.2010.5674577.

[35]

Rodríguez-Espíndola O, Gaytán J. Scenario-based preparedness plan for floods. Natural Hazards, 2015, 76(2): 1241-1262

[36]

Saadatseresht M, Mansourian A, Taleai M. Evacuation planning using multiobjective evolutionary optimization approach. European Journal of Operational Research, 2009, 198(1): 305-314

[37]

Sherali HD, Carter TB, Hobeika AG. A location–allocation model and algorithm for evacuation planning under hurricane/flood conditions. Transportation Research Part B: Methodological, 1991, 25(6): 439-452

[38]

Toregas C, Swain R, ReVelle C, Bergman L. The location of emergency service facilities. Operations Research, 1971, 19(6): 1363-1373

[39]

Weber A. Theory of the location of industries (trans. C.J. Friedrich from Weber’s 1909 book), 1929, Chicago: The University of Chicago Press

[40]

Widener, M.J. 2009. Modeling hurricane disaster relief distribution with a hierarchical capacitated-median model: An analysis with extensions. Master’s thesis. Florida State University, Tallahassee, FL 32306, USA.

[41]

Widener MJ, Horner MW. A hierarchical approach to modeling hurricane disaster relief goods distribution. Journal of Transport Geography, 2011, 19(4): 821-828

[42]

Wood NJ, Schmidtlein MC. Community variations in population exposure to near-field tsunami hazards as a function of pedestrian travel time to safety. Natural Hazards, 2013, 65(3): 1603-1628

[43]

Xu W, Ma Y, Zhao X, Li Y, Qin L, Du J. A comparison of scenario-based hybrid bilevel and multi-objective location–allocation models for earthquake emergency shelters: A case study in the central area of Beijing, China. International Journal of Geographical Information Science, 2018, 32(2): 236-256

[44]

Yeh WC. A two-stage discrete particle swarm optimization for the problem of multiple multi-level redundancy allocation in series systems. Expert Systems with Applications, 2009, 36(5): 9192-9200

[45]

Yin P, Yu S, Wang P, Wang Y. Multi-objective task allocation in distributed computing systems by hybrid particle swarm optimization. Applied Mathematics Computation, 2007, 184: 407-420

[46]

Yu J, Wen J. Multi-criteria satisfaction assessment of the spatial distribution of urban emergency shelters based on high-precision population estimation. International Journal of Disaster Risk Science, 2016, 7(4): 429-431

[47]

Zahran S, Tavani D, Weiler S. Daily variation in natural disaster casualties: Information flows, safety, and opportunity costs in tornado versus hurricane strikes. Risk Analysis, 2013, 33(7): 1265-1280

[48]

Zhang N, Huang H, Su B, Zhang H. Population evacuation analysis: considering dynamic population vulnerability distribution and disaster information dissemination. Natural Hazards, 2013, 69(3): 1629-1646

[49]

Zhao X, Xu W, Ma Y, Hu F. Scenario-based multi-objective optimum allocation model for earthquake emergency shelters using a modified particle swarm optimization algorithm: a case study in Chaoyang District, Beijing, China. PLoS ONE, 2015, 10(12): e0144455

AI Summary AI Mindmap
PDF

190

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/