Simulation-Based and Risk-Informed Assessment of the Effectiveness of Tsunami Evacuation Routes Using Agent-Based Modeling: A Case Study of Seaside, Oregon

Zhenqiang Wang , Gaofeng Jia

International Journal of Disaster Risk Science ›› 2022, Vol. 13 ›› Issue (1) : 66 -86.

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International Journal of Disaster Risk Science ›› 2022, Vol. 13 ›› Issue (1) : 66 -86. DOI: 10.1007/s13753-021-00387-x
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Simulation-Based and Risk-Informed Assessment of the Effectiveness of Tsunami Evacuation Routes Using Agent-Based Modeling: A Case Study of Seaside, Oregon

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Abstract

Typically, tsunami evacuation routes are marked using signs in the transportation network and the evacuation map is made to educate people on how to follow the evacuation route. However, tsunami evacuation routes are usually identified without the support of evacuation simulation, and the route effectiveness in the reduction of evacuation risk is typically unknown quantitatively. This study proposes a simulation-based and risk-informed framework for quantitative evaluation of the effectiveness of evacuation routes in reducing evacuation risk. An agent-based model is used to simulate the tsunami evacuation, which is then used in a simulation-based risk assessment framework to evaluate the evacuation risk. The route effectiveness in reducing the evacuation risk is evaluated by investigating how the evacuation risk varies with the proportion of the evacuees that use the evacuation route. The impacts of critical risk factors such as evacuation mode (for example, on foot or by car) and population size and distribution on the route effectiveness are also investigated. The evacuation risks under different cases are efficiently calculated using the augmented sample-based approach. The proposed approach is applied to the risk-informed evaluation of the route effectiveness for tsunami evacuation in Seaside, Oregon. The evaluation results show that the route usage is overall effective in reducing the evacuation risk in the study area. The results can be used for evacuation preparedness education and hence effective evacuation.

Keywords

Agent-based model / Risk-informed evaluation / Route effectiveness / Simulation-based framework / Tsunami evacuation risk / Tsunami preparedness education

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Zhenqiang Wang, Gaofeng Jia. Simulation-Based and Risk-Informed Assessment of the Effectiveness of Tsunami Evacuation Routes Using Agent-Based Modeling: A Case Study of Seaside, Oregon. International Journal of Disaster Risk Science, 2022, 13(1): 66-86 DOI:10.1007/s13753-021-00387-x

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References

[1]

Adiyoso W, Kanegae H. The effect of different disaster education programs on tsunami preparedness among schoolchildren in Aceh, Indonesia. Disaster Mitigation of Cultural Heritage and Historic Cities, 2012, 6(1): 165-172.

[2]

Aguilar L, Wijerathne L, Jacob S, Hori M, Ichimura T. Mass evacuation simulation considering detailed models: Behavioral profiles, environmental effects, and mixed-mode evacuation. Asia Pacific Management Review, 2019, 24(2): 114-123

[3]

Ahlström, C., A. Anund, C. Fors, and T. Åkerstedt. 2018. The effect of daylight versus darkness on driver sleepiness: A driving simulator study. Journal of Sleep Research 27(3): Article e12642.

[4]

Ai F, Comfort LK, Dong Y, Znati T. A dynamic decision support system based on geographical information and mobile social networks: A model for tsunami risk mitigation in Padang, Indonesia. Safety Science, 2016, 90: 62-74

[5]

Bernard EN. Developing tsunami-resilient communities: The national tsunami hazard mitigation program, 2005, Heidelberg: Springer Science & Business Media

[6]

Campbell, K.W., and Y. Bozorgnia. 2008. NGA ground motion model for the geometric mean horizontal component of PGA, PGV, PGD and 5% damped linear elastic response spectra for periods ranging from 0.01 to 10 s. Earthquake Spectra 24(1): 139–171.

[7]

City of Rockaway Beach, Oregon. 2019. Oregon tsunami evacuation facilities improvement plan (TEFIP). Technical report. City of Rockaway Beach, Oregon, USA.

[8]

City of Seaside. 2010. Seaside transportation system plan volume I: Plan. Technical report. City of Seaside, Oregon, USA.

[9]

Claptsop County. 2008.Seaside ArcGIS shapefiles. Released 2008. http://web.pdx.edu/˜jduh/seasidegis/shapefiles/main.php. Accessed 4 Sept 2020.

[10]

Clatsop County. 2019. Oregon transportation map showing federal functional classification of roads. Released 2019. City of Seaside, Oregon, USA. https://www.oregon.gov/odot/Data/Documents/City Seaside.pdf. Accessed 4 Sept 2020.

[11]

Dall’Osso, F., and D. Dominey-Howes. Public assessment of the usefulness of “draft” tsunami evacuation maps from Sydney, Australia – Implications for the establishment of formal evacuation plans. Natural Hazards and Earth System Sciences, 2010, 10(8): 1739-1750

[12]

Darienzo, M. 2001. Oregon’s 5-year tsunami activity report, 1997–2001. In Proceedings of International Tsunami Symposium 2001, 7–10 August 2001, Seattle, WA, USA, 209–211.

[13]

Dengler L. The role of education in the national tsunami hazard mitigation program. Natural Hazards, 2005, 35(1): 141-153

[14]

DHS (Department of Homeland Security) HAZUS-MH MR4 earthquake model user manual, 2009, Washington, DC: Department of Homeland Security, Federal Emergency Management Agency, Mitigation Division

[15]

DOGAMI (Oregon Department of Geology and Mineral Industries). 2013. Tsunami evacuation map Seaside & Gearhart, Oregon. Released 3 June 2013. https://www.oregongeology.org/pubs/tsubrochures/SeasideGearhartEvacBrochure-63-13 onscreen.pdf. Accessed 4 Sept 2020.

[16]

DOGAMI (Oregon Department of Geology and Mineral Industries). 2020a. Large-extent tsunami evacuation maps. https://www.oregongeology.org/tsuclearinghouse/pubs-evacbro.htm. Accessed 14 Dec 2020.

[17]

DOGAMI (Oregon Department of Geology and Mineral Industries). 2020b. Tsunami evacuation maps. https://www.oregongeology.org/tsuclearinghouse/tsunami-evacuation-maps.htm. Accessed 14 Dec 2020.

[18]

Dwelley Samant, L., L.T. Tobin, and B. Tucker. 2008. Preparing your community for tsunamis: A guidebook for local advocates. Working draft version 2.1. Menlo Park, CA: GeoHazards International.

[19]

Forcael E, González V, Orozco F, Vargas S, Pantoja A, Moscoso P. Ant colony optimization model for tsunamis evacuation routes. Computer-Aided Civil and Infrastructure Engineering, 2014, 29(10): 723-737

[20]

González, F.I., V.V. Titov, H.O. Mofjeld, A.J. Venturato, and J.C. Newman. 2001. The NTHMP inundation mapping program. In Proceedings of International Tsunami Symposium 2001, 7–10 August 2001, Seattle, WA, USA, 29–54.

[21]

González FI, Titov VV, Mofjeld HO, Venturato AJ, Simmons RS, Hansen R, Combellick R, Eisner RK Progress in NTHMP hazard assessment. Natural Hazards, 2005, 35(1): 89-110

[22]

Group, T.P.S.W. 2006. Seaside, Oregon tsunami pilot study: Modernization of FEMA flood hazard maps. Menlo Park, CA: United States Geological Survey.

[23]

Hajo Neis, H.J., H. Pempus, P. Wright, and K. Nolte. 2015. Up and out 2. Technical report. The Portland Urban Architecture Research Laboratory Portland, OR, USA.

[24]

Imamura F, Muhari A, Mas E, Pradono MH, Post J, Sugimoto M. Tsunami disaster mitigation by integrating comprehensive countermeasures in Padang City, Indonesia. Journal of Disaster Research, 2012, 7(1): 48-64

[25]

Jacob S, Aguilar L, Wijerathne L, Hori M, Ichimura T, Tanaka S. Agent based modeling and simulation of tsunami triggered mass evacuation considering changes of environment due to earthquake and inundation. Journal of Japan Society of Civil Engineers, 2014, 70(2): 671-680.

[26]

Jia G, Taflanidis AA. Sample-based evaluation of global probabilistic sensitivity measures. Computers and Structures, 2014, 144: 103-118

[27]

Jia, G., and A.A. Taflanidis. 2016. Efficient evaluation of Sobol’ indices utilizing samples from an auxiliary probability density function. Journal of Engineering Mechanics 142(5): Article 04016012.

[28]

Jia, G., A.A. Taflanidis, and J.L. Beck. 2017. A new adaptive rejection sampling method using kernel density approximations and its application to subset simulation. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 3(2): Article D4015001.

[29]

Kurowski MJ, Hedley N, Clague JJ. An assessment of educational tsunami evacuation map designs in Washington and Oregon. Natural Hazards, 2011, 59(2): 1205-1223

[30]

León J, March A. An urban form response to disaster vulnerability: Improving tsunami evacuation in Iquique, Chile. Environment and Planning B: Planning and Design, 2016, 43(5): 826-847

[31]

Limanond T, Kim H, Siridhara S, Tipakornkiat C, Chermkhunthod C, Uttra S. Decision on tsunami evacuation route in tourism area: A case study of Had Patong, Phuket. Journal of the Eastern Asia Society for Transportation Studies, 2011, 9: 16-30.

[32]

Lonergan C, Hedley N, Clague JJ. A visibility-based assessment of tsunami evacuation signs in Seaside, Oregon. Natural Hazards, 2015, 78(1): 41-59

[33]

Løvholt, F., S. Fraser, M. Salgado-Gálvez, S. Lorito, J. Selva, F. Romano, A. Suppasri, E. Mas, et al. 2019. Global trends in advancing tsunami science for improved hazard and risk understanding. Contributing paper to Global Assessment Report on Disaster Risk Reduction (GAR 2019). Geneva: United Nations Office for Disaster Risk Reduction.

[34]

Mas E, Suppasri A, Imamura F, Koshimura S. Agent-based simulation of the 2011 Great East Japan Earthquake/Tsunami evacuation: An integrated model of tsunami inundation and evacuation. Journal of Natural Disaster Science, 2012, 34(1): 41-57

[35]

Mostafizi A, Wang H, Cox D, Cramer LA, Dong S. Agent-based tsunami evacuation modeling of unplanned network disruptions for evidence-driven resource allocation and retrofitting strategies. Natural Hazards, 2017, 88(3): 1347-1372

[36]

Mostafizi A, Wang H, Dong S. Understanding the multimodal evacuation behavior for a near-field tsunami. Transportation Research Record, 2019, 2673(11): 480-492

[37]

Murakami, H., S. Yanagihara, Y. Goto, T. Mikami, S. Sato, and T. Wakihama. 2014. Study on casualty and tsunami evacuation behavior in Ishinomaki City – Questionnaire survey for the 2011 Great East Japan Earthquake. In Proceedings of 10th U.S. National Conference on Earthquake Engineering: Frontiers of Earthquake Engineering, 21–25 July 2014, Anchorage, Alaska, USA.

[38]

NTHMP (National Tsunami Hazard Mitigation Program). 2018. Accomplishments of the National Tsunami Hazard Mitigation Program: An annual report. Technical report. Washington, DC: National Oceanic and Atmospheric Administration.

[39]

ODOT (Oregon Department of Transportation). 2020. Speed zone manual. Technical report. Statewide Project Delivery Branch – Engineering & Technical Services Branch Traffic-Roadway Section, Salem, Oregon, USA.

[40]

Ouellette MJ, Rea MS. Illuminance requirements for emergency lighting. Journal of the Illuminating Engineering Society, 1989, 18(1): 37-42

[41]

Patel, N., M. Min, and S. Lim. 2016. Accurate evacuation route planning using forward-backward shortest paths. In Proceedings of 2016 Annual IEEE Systems Conference (SysCon), 18–21 April 2016, Orlando, FL, USA, 1–6.

[42]

Péroche M, Leone F, Gutton R. An accessibility graph-based model to optimize tsunami evacuation sites and routes in Martinique, France. Advances in Geosciences, 2014, 38: 1-8

[43]

Priest, G.R., A.M. Baptista, E. Myers III, and R. Kamphaus. 2001. Tsunami hazard assessment in Oregon. In Proceedings of International Tsunami Symposium, 7–10 August 2001, Seattle, WA, USA, 55–65.

[44]

Priest GR, Stimely LL, Wood NJ, Madin IP, Watzig RJ. Beat-the wave evacuation mapping for tsunami hazards in Seaside, Oregon, USA. Natural Hazards, 2016, 80(2): 1031-1056

[45]

Robert CP, Casella G. Monte Carlo statistical methods, 2004 2 New York: Springer

[46]

Scheer S, Gardi A, Guillande R, Eftichidis G, Varela V, de Vanssay B, ColbeauJustin L. Handbook of tsunami evacuation planning, 2011, Luxembourg City, Luxembourg: Publications Office of the European Union

[47]

Schuster, M., and C. Gomez. 2013. Evacuation routing out of tsunami hazard zones. In Proceedings of the Geoinformatics Forum 2013, 2–5 July 2013, Salzburg, Austria, 206–215.

[48]

Shekhar S, Yang KS, Gunturi VM, Manikonda L, Oliver D, Zhou X, George B, Kim S Experiences with evacuation route planning algorithms. International Journal of Geographical Information Science, 2012, 26(12): 2253-2265

[49]

Sleeter, R., and N. Wood. 2006. Estimating daytime and nighttime population density for coastal communities in Oregon. In Proceedings of 44th Urban and Regional Information Systems Association Annual Conference, 26–29 September 2006, British Columbia, Canada.

[50]

Suppasri A, Muhari A, Ranasinghe P, Mas E, Shuto N, Imamura F, Koshimura S. Damage and reconstruction after the 2004 Indian Ocean tsunami and the 2011 Great East Japan Tsunami. Journal of Natural Disaster Science, 2012, 34(1): 19-39

[51]

Suppasri A, Shuto N, Imamura F, Koshimura S, Mas E, Yalciner AC. Lessons learned from the 2011 Great East Japan Tsunami: Performance of tsunami countermeasures, coastal buildings, and tsunami evacuation in Japan. Pure and Applied Geophysics, 2013, 170(6): 993-1018

[52]

Takabatake T, Shibayama T, Esteban M, Ishii H, Hamano G. Simulated tsunami evacuation behavior of local residents and visitors in Kamakura, Japan. International Journal of Disaster Risk Reduction, 2017, 23: 1-14

[53]

Tanioka Y, Latief H, Sunendar H, Gusman AR, Koshimura S. Tsunami hazard mitigation at Palabuhanratu, Indonesia. Journal of Disaster Research, 2012, 7(1): 19-25

[54]

Titov, V.V., and F.I. González. 1997. Implementation and testing of the method of splitting tsunami (MOST) model. Technical report. Seattle, WA: National Oceanic and Atmospheric Administration.

[55]

Trindade A, Teves-Costa P, Catita C. A GIS-based analysis of constraints on pedestrian tsunami evacuation routes: Cascais case study (Portugal). Natural Hazards, 2018, 93(1): 169-185

[56]

UNESCO (United Nations Educational, Scientific and Cultural Organization). 2007. Tsunami risk assessment and mitigation for the Indian Ocean: Knowing your tsunami risk—and what to do about it. IOC Manual and Guides No. 52. Paris: UNESCO.

[57]

U.S. Census Bureau. 2018. 2018 census. https://www.census.gov/. Accessed 20 Jan 2020.

[58]

USGS (U.S. Geological Survey). 2020. Cascadiasubductionzone. https://www.usgs.gov/media/images/cascadia-subduction-zone-1. Accessed 20 Jan 2020.

[59]

Wang, Z. 2021. Simulation-based tsunami evacuation risk assessment and risk-informed mitigation. Ph.D. dissertation. Fort Collins, CO, Colorado State University.

[60]

Wang Z, Jia G. A novel agent-based model for tsunami evacuation simulation and risk assessment. Natural Hazards, 2021, 105(2): 2045-2071

[61]

Wang, Z., and G. Jia. 2021b. Tsunami evacuation risk assessment and probabilistic sensitivity analysis using augmented sample-based approach. International Journal of Disaster Risk Reduction 63: Article 102462.

[62]

Wang H, Mostafizi A, Cramer LA, Cox D, Park H. An agent-based model of a multimodal near-field tsunami evacuation: Decision-making and life safety. Transportation Research Part C: Emerging Technologies, 2016, 64: 86-100

[63]

Wilensky, U. 2001. Modeling nature’s emergent patterns with multi-agent languages. In Proceedings of EuroLogo 2001, 21–25 August 2001, Linz, Austria.

[64]

Yamamoto, K., and X. Li. 2017. Safety evaluation of evacuation routes in central Tokyo assuming a large-scale evacuation in case of earthquake disasters. Journal of Risk and Financial Management 10: Article 14.

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