1,127
Views
4
CrossRef citations to date
0
Altmetric
Research Article

Integrating multi-agent evacuation simulation and multi-criteria evaluation for spatial allocation of urban emergency shelters

, ORCID Icon, , ORCID Icon, &
Pages 1884-1910 | Received 29 May 2017, Accepted 07 Apr 2018, Published online: 25 Apr 2018

References

  • Alçada-Almeida, L., et al., 2009. A multiobjective approach to locate emergency shelters and identify evacuation routes in urban areas. Geographical Analysis, 41 (1), 9–29. doi:10.1111/j.1538-4632.2009.00745.x
  • Anhorn, J. and Khazai, B., 2015. Open space suitability analysis for emergency shelter after an earthquake. Natural Hazards and Earth System Sciences, 15, 789–803. doi:10.5194/nhess-15-789-2015
  • Bakillah, M., et al., 2013. Multi-agent evacuation simulation data model with social considerations for disaster management context. In: S. Zlatanova, et al., eds. Intelligent systems for crisis management. Lecture Notes in Geoinformation and Cartography. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 3–16.
  • Bashawri, A., Garrity, S., and Moodley, K., 2014. An overview of the design of disaster relief shelters. Procedia Economics and Finance, 18, 924–931.
  • Bayram, V., Tansel, B.Ç., and Yaman, H., 2015. Compromising system and user interests in shelter location and evacuation planning. Transportation Research Part B: Methodological, 72, 146–163. doi:10.1016/j.trb.2014.11.010
  • Bellomo, N., Piccoli, B., and Tosin, A., 2012. Modeling crowd dynamics from a complex system viewpoint. Mathematical Models and Methods in Applied Sciences, 22 (supp02), 1230004. doi:10.1142/S0218202512300049
  • Berseth, G., et al., 2015. Environment optimization for crowd evacuation. Computer Animation and Virtual Worlds, 26 (3–4), 377–386. doi:10.1002/cav.v26.3-4
  • Borrmann, A., et al., 2012. Bidirectional coupling of macroscopic and microscopic pedestrian evacuation models. Safety Science, 50 (8), 1695–1703. doi:10.1016/j.ssci.2011.12.021
  • Botev, Z.I., Grotowski, J.F., and Kroese, D.P., 2010. Kernel density estimation via diffusion. The Annals of Statistics, 38 (5), 2916–2957. doi:10.1214/10-AOS799
  • Chang, H. and Liao, C., 2014. Planning emergency shelter locations based on evacuation behavior. Natural Hazards, 76 (3), 1551–1571. doi:10.1007/s11069-014-1557-x
  • Chen, H. and Kocaoglu, D.F., 2008. A sensitivity analysis algorithm for hierarchical decision models. European Journal of Operational Research, 185 (1), 266–288. doi:10.1016/j.ejor.2006.12.029
  • Chen, P., et al., 2014a. Evaluation of resident evacuations in urban rainstorm waterlogging disasters based on scenario simulation: Daoli District (Harbin, China) as an example. International Journal of Environmental Research and Public Health, 11, 9964–9980. doi:10.3390/ijerph111009964
  • Chen, W., et al., 2016. A planning framework based on system theory and GIS for urban emergency shelter system: a case of Guangzhou, China. Human and Ecological Risk Assessment, an International Journal, 23 (3), 441–456. doi:10.1080/10807039.2016.1185692
  • Chen, Y., et al., 2014b. Path optimization study for vehicles evacuation based on Dijkstra algorithm. Procedia Engineering, 71, 159–165. doi:10.1016/j.proeng.2014.04.023
  • Chen, Y., et al., 2015. A spatial assessment framework for evaluating flood risk under extreme climates. Science of the Total Environment, 538, 512–523. doi:10.1016/j.scitotenv.2015.08.094
  • Chen, Y., Yu, J., and Khan, S., 2010. Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation. Environmental Modelling & Software, 25 (12), 1582–1591. doi:10.1016/j.envsoft.2010.06.001
  • Chen, Y., Yu, J., and Khan, S., 2013. The spatial framework for weight sensitivity analysis in AHP-based multi-criteria decision making. Environmental Modelling & Software, 48, 129–140. doi:10.1016/j.envsoft.2013.06.010
  • Chollet, F., Tixier, J., and Slangen, P., 2016. Training decision-makers: existing strategies for natural and technological crisis management and specifications of an improved simulation-based tool. Safety Science, 97, 144–153. doi:10.1016/j.ssci.2016.03.025
  • Dawson, R.J., Peppe, R., and Wang, M., 2011. An agent-based model for risk-based flood incident management. Natural Hazards, 59 (1), 167–189. doi:10.1007/s11069-011-9745-4
  • Elgar, I., Farooq, B., and Miller, E.J., 2015. Simulations of firm location decisions: replicating office location choices in the Greater Toronto Area. Journal of Choice Modelling, 17, 39–51. doi:10.1016/j.jocm.2015.12.003
  • Ertugay, K., Argyroudis, S., and Düzgün, H.Ş., 2016. Accessibility modeling in earthquake case considering road closure probabilities: a case study of health and shelter service accessibility in Thessaloniki, Greece. International Journal of Disaster Risk Reduction, 17, 49–66. doi:10.1016/j.ijdrr.2016.03.005
  • Ferrand, N., 1996. Modeling and supporting multi-actor spatial planning using multi-agents systems. In: Third NCGIA conference on integrating GIS and environmental modelling. Santa Fe, New Mexico: NCGIA.
  • Fiedrich, F., 2007. Agent-based systems for disaster management. Communications of the ACM, 50 (3), 41–42. doi:10.1145/1226736.1226763
  • Filatova, T., et al., 2013. Spatial agent-based models for socio-ecological systems: challenges and prospects. Environmental Modelling & Software, 45, 1–7. doi:10.1016/j.envsoft.2013.03.017
  • Gaube, V. and Remesch, A., 2013. Impact of urban planning on household’s residential decisions: an agent-based simulation model for Vienna. Environmental Modelling & Software, 45, 92–103. doi:10.1016/j.envsoft.2012.11.012
  • Ge, X., Wei, D., and Jin, H., 2011. Study on the social psychology and behaviors in a subway evacuation drill in China. Procedia Engineering, 11, 112–119. doi:10.1016/j.proeng.2011.04.635
  • Georgoudas, I.G., Sirakoulis, G.C., and Andreadis, I.T., 2010. An anticipative crowd management system preventing clogging in exits during pedestrian evacuation processes. IEEE Systems Journal, 5 (1), 129–141. doi:10.1109/JSYST.2010.2090400
  • Greger, K., 2015. Spatio-temporal building population estimation for highly urbanized areas using GIS. Transactions in GIS, 19 (1), 129–150. doi:10.1111/tgis.2015.19.issue-1
  • Gul, M. and Guneri, A.F., 2015. A comprehensive review of emergency department simulation applications for normal and disaster conditions. Computers & Industrial Engineering, 83 (C), 327–344. doi:10.1016/j.cie.2015.02.018
  • Hainesa, A., et al., 2006. Climate change and human health: impacts, vulnerability and public health. Public Health, 120 (7), 585–596. doi:10.1016/j.puhe.2006.01.002
  • Hamacher, H.W. and Tjandra, S.A., 2002. Mathematical modelling of evacuation problems: a state of the art. In: M. Schreckenberg and S.D. Sharma, eds. Pedestrian and evacuation dynamics. Berlin Heidelberg: Springer, 227–266.
  • Hashemi, M. and Alesheikh, A.A., 2013. GIS: agent-based modeling and evaluation of an earthquake-stricken area with a case study in Tehran, Iran. Natural Hazards, 69 (3), 1895–1917. doi:10.1007/s11069-013-0784-x
  • Helbing, D., et al., 2002. Simulation of pedestrian crowds in normal and evacuation simulations. In: M. Schreckenberg and S.D. Sharma, eds. Pedestrian and evacuation dynamics. Berlin Heidelberg: Springer, 21–58.
  • Hu, F., Xu, W., and Li, X., 2012. A modified particle swarm optimization algorithm for optimal allocation of earthquake emergency shelters. International Journal of Geographical Information Science, 26 (9), 1643–1666. doi:10.1080/13658816.2011.643802
  • Huang, D., et al., 2006. Emergency adaption of urban emergency shelter: analytic hierarchy process-based assessment method. Journal of Natural Disasters, 15 (1), 52–58.
  • Ivanov, S.V., et al., 2012. Simulation-based collaborative decision support for surge floods prevention in St. Petersburg. Journal of Computational Science, 3 (6), 450–455. doi:10.1016/j.jocs.2012.08.005
  • Jaziri, W. and Paquet, T., 2006. A multi-agent model and Tabu search optimization to manage agricultural territories. GeoInformatica, 10, 337–357. doi:10.1007/s10707-006-9831-z
  • Joo, J., et al., 2013. Agent-based simulation of affordance-based human behaviors in emergency evacuation. Simulation Modelling Practice and Theory, 32, 99–115. doi:10.1016/j.simpat.2012.12.007
  • Kawai, J., Mitsuhara, H., and Shishibori, M., 2015. Tsunami evacuation drill system using smart glasses. Procedia Computer Science, 72, 329–336. doi:10.1016/j.procs.2015.12.147
  • Kılcı, F., Kara, B.Y., and Bozkaya, B., 2015. Locating temporary shelter areas after an earthquake: a case for Turkey. European Journal of Operational Research, 243 (1), 323–332. doi:10.1016/j.ejor.2014.11.035
  • Lam, S.S.W., et al., 2017. Simulation-based decision support framework for dynamic ambulance redeployment in Singapore. International Journal of Medical Informatics, 106, 37–47. doi:10.1016/j.ijmedinf.2017.06.005
  • Li, X. and Yeh, G.O., 2002. Integration of principal components analysis and cellular automata for spatial decisionmaking and urban simulation. Science in China (Series D), 45 (6), 521–529. doi:10.1360/02yd9054
  • Liu, Q., Ruan, X., and Shi, P., 2011. Selection of emergency shelter sites for seismic disasters in mountainous regions: lessons from the 2008 Wenchuan Ms 8.0 earthquake, China. Journal of Asian Earth Sciences, 40 (4), 926–934. doi:10.1016/j.jseaes.2010.07.014
  • Liu, X. and Li, X., 2006. Multi-agent systems for simulating spatial decision behaviors and land use dynamics. Science in China (Series D), 49 (11), 1184–1194. doi:10.1007/s11430-006-1184-9
  • Liu, X. and Lim, S., 2016. Integration of spatial analysis and an agent-based model into evacuation management for shelter assignment and routing. Journal of Spatial Science, 61 (2), 283–298. doi:10.1080/14498596.2016.1147393
  • Lwin, K.K. and Murayama, Y., 2011. Estimation of building population from LIDAR derived digital volume model. In: Y. Murayama and R.B. Thapa, eds. Spatial analysis and modeling in geographical transformation process. The GeoJournal library. Dordrecht (Netherlands): Springer, 87–98.
  • Ma, D.X., et al., 2011. Study on evaluation of earthquake evacuation capacity in village based on multi-level Grey evaluation. Systems Engineering Procedia, 1, 85–92. doi:10.1016/j.sepro.2011.08.015
  • Ma, Y., Lee, E.W.M., and Yuen, R.K.K., 2017. Dual effects of pedestrian density on emergency evacuation. Physics Letters A, 381 (5), 435–439. doi:10.1016/j.physleta.2016.11.043
  • Maantay, J.A., Maroko, A.R., and Herrmann, C., 2007. Mapping population distribution in the urban environment: the cadastral-based expert dasymetric system (CEDS). Cartography and Geographic Information Science, 34 (2), 77–102. doi:10.1559/152304007781002190
  • Malczewski, J., 2000. On the use of weighted linear combination method in GIS: common and best practice approaches. Transactions in GIS, 4, 5–22. doi:10.1111/1467-9671.00035
  • Manley, E., et al., 2014. A framework for simulating large-scale complex urban traffic dynamics through hybrid agent-based modelling. Computers, Environment and Urban Systems, 44, 27–36. doi:10.1016/j.compenvurbsys.2013.11.003
  • Mas, E., et al., 2015. Recent advances in agent-based tsunami evacuation simulations: case studies in Indonesia, Thailand, Japan and Peru. Pure and Applied Geophysics, 172 (12), 3409–3424. doi:10.1007/s00024-015-1105-y
  • Massaguer, D., et al., 2006. Multi-agent simulation of disaster response. In: A. Tate, et al. eds. Proceedings of the first international workshop on agent technology for disaster management. Hakodate, Hokkaido, Japan, CiteSeer, 124–130.
  • Masuya, A., Dewan, A., and Corner, R.J., 2015. Population evacuation: evaluating spatial distribution of flood shelters and vulnerable residential units in Dhaka with geographic information systems. Natural Hazards, 78 (3), 1859–1882. doi:10.1007/s11069-015-1802-y
  • Ohta, K., et al., 2007. Analysis of the geographical accessibility of neurosurgical emergency hospitals in Sapporo city using GIS and AHP. International Journal of Geographical Information Science, 21 (6), 687–698. doi:10.1080/13658810601135692
  • Orencio, P.M. and Fujii, M., 2013. A localized disaster-resilience index to assess coastal communities based on an analytic hierarchy process (AHP). International Journal of Disaster Risk Reduction, 3, 62–75. doi:10.1016/j.ijdrr.2012.11.006
  • Ormerod, P. and Rosewell, B., 2009. Validation and verification of agent-based models in the social sciences. In: F. Squazzoni, ed. Epistemological aspects of computer simulation in the social sciences. Berlin: Springer, 130–140.
  • Pan, A., 2010. The applications of maximal covering model in typhoon emergency shelter location problem. In: Industrial engineering and engineering management (IEEM), 2010 IEEE international conference. Piscataway, NJ: IEEE, 1727–1731.
  • Roytman, M.Y., 1975. Principles of fire safety standards for building constructions. New Delhi: Amerind Publish Co.
  • Saaty, T.L., 1977. A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15 (3), 234–281. doi:10.1016/0022-2496(77)90033-5
  • Srinivasa, H. and Nakagawa, Y., 2008. Environmental implications for disaster preparedness: lessons learnt from the Indian Ocean Tsunami. Journal of Environmental Management, 89, 4–13. doi:10.1016/j.jenvman.2007.01.054
  • Tai, C., Lee, Y., and Lin, C., 2010. Urban disaster prevention shelter location and evacuation behavior analysis. Journal of Asian Architecture and Building Engineering, 9 (1), 215–220. doi:10.3130/jaabe.9.215
  • Tan, L., Hu, M., and Lin, H., 2015b. Agent-based simulation of building evacuation: combining human behavior with predictable spatial accessibility in a fire emergency. Information Sciences, 295, 53–66. doi:10.1016/j.ins.2014.09.029
  • Tan, L., Wu, L., and Lin, H., 2015a. An individual cognitive evacuation behaviour model for agent-based simulation: a case study of a large outdoor event. International Journal of Geographical Information Science, 29 (9), 1552–1568. doi:10.1080/13658816.2015.1030751
  • Tavares, R.M., 2010. Design for horizontal escape in buildings: the use of the relative distance between exits as an alternative approach to the maximum travel distance. Safety Science, 48 (10), 1242–1247. doi:10.1016/j.ssci.2010.03.009
  • Thompson, P.A. and Marchant, E.W., 1994. Simulex; developing new computer modelling techniques for evaluation. Fire Safety Science, 4, 613–624. doi:10.3801/IAFSS.FSS.4-613
  • Tong, Z., et al., 2012. GIS-based design of urban emergency shelter in Songbei Harbin. In: Z. Qian, eds. Recent advances in computer science and information engineering. Berlin: Springer, 617–622.
  • Trivedi, A. and Singh, A., 2017. A hybrid multi-objective decision model for emergency shelter location-relocation projects using fuzzy analytic hierarchy process and goal programming approach. International Journal of Project Management, 35 (5), 827–840. doi:10.1016/j.ijproman.2016.12.004
  • Vetter, M., et al., 2005. Partitioning direct and indirect human-induced effects on carbon sequestration of managed coniferous forests using model simulations and forest inventories. Global Change Biology, 11, 810–827. doi:10.1111/gcb.2005.11.issue-5
  • Widener, M.J., Horner, M.W., and Metcalf, S.S., 2013. Simulating the effects of social networks on a population’s hurricane evacuation participation. Journal of Geographical Systems, 15, 193–209. doi:10.1007/s10109-012-0170-3
  • Xu, J., et al., 2016. Multi-criteria location model of earthquake evacuation shelters to aid in urban planning. International Journal of Disaster Risk Reduction, 20, 51–62. doi:10.1016/j.ijdrr.2016.10.009
  • Xu, W., et al., 2014. Collaborative modelling-based shelter planning analysis: a case study of the Nagata Elementary School Community in Kobe City, Japan. Disasters, 38 (1), 125–147. doi:10.1111/disa.2014.38.issue-1
  • Yang, X., et al., 2012. A fuzzy AHP-TFN based evaluation model of flood risk analysis. Journal of Computational Information Systems, 8 (22), 9281–9289.
  • Yu, J. and Wen, J., 2016. Multi-criteria satisfaction assessment of the spatial distribution of urban emergency shelters based on high-precision population estimation. International Journal of Disaster Risk Science, 7 (4), 413–429. doi:10.1007/s13753-016-0111-8
  • Yu, Y., et al., 2014. Multi-agent based modeling and simulation of microscopic traffic in virtual reality system. Simulation Modelling Practice and Theory, 45, 62–79. doi:10.1016/j.simpat.2014.04.001
  • Zhang, H., et al., 2015. Multi-agent based modeling of spatiotemporal dynamical urban growth in developing countries: simulating future scenarios of Lianyungang city, China. Stochastic Environmental Research and Risk Assessment, 29, 63–78. doi:10.1007/s00477-014-0942-z
  • Zou, Y., et al., 2012. Accelerating agent-based computation of complex urban systems. International Journal of Geographical Information Science, 26 (10), 1917–1937. doi:10.1080/13658816.2012.669891

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.