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http://dx.doi.org/10.1016/j.net.2022.01.035

Effects of child pick-up behavior on emergency evacuations  

Jang, Sang Hoon (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Hwang, Ha (Division of Disaster & Safety Research, Korea Institute of Public Administration)
Chung, Ji-Bum (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Publication Information
Nuclear Engineering and Technology / v.54, no.7, 2022 , pp. 2519-2528 More about this Journal
Abstract
The child pick-up behavior of parents during an emergency can cause heavy traffic congestion and failing to evacuate an affected area successfully. In this study, we analyzed the effect of child pick-up behavior using, as an example, a nuclear power plant accident caused by an earthquake, which is a typical no-notice emergency. A quake was assumed to occur near the Shin-Kori nuclear power plant in Ulsan, Korea, resulting in a nuclear power plant accident. An agent-based dynamic simulation model using VISSIM was employed to conduct sensitivity analyses with different child pick-up rates. The results confirmed that parents are a major cause of congestion and a vulnerable class in an emergency evacuation. The child pick-up behavior caused significant traffic congestion, and parents who pick up their children showed a higher evacuation failure rate.
Keywords
Child pick-up behavior; Agent-based modeling; Shin-kori nuclear power plant; Earthquake; VISSIM;
Citations & Related Records
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1 R. Goldblatt, Evacuation planning, human factors and traffic engineering perspectives, in: Proceedings of the 2004 European Transport Conference, Strasbourg, France, 2004.
2 P. Edara, S. Sharma, C. McGhee, Development of a large-scale traffic simulation model for hurricane evacuation-methodology and lessons learned, Nat. Hazards Rev. 11 (4) (2010) 127-139.   DOI
3 M. Malesic, et al., Evacuation in the event of a nuclear disaster: planned activity or improvisation? Int. J. Disaster Risk Reduc. 12 (2015) 102-111.   DOI
4 E.E. Tuncer, Operational Impact of Shadow Evacuation on Regional Road Networks during Short-Notice Emergency Evacuations, LSU Master's Theses, 2018.
5 Chung, A Study on the Master Plan for Earthquake Prevention in Ulsan, 2018.
6 T. Urbanik, Evacuation time estimates for nuclear power plants, J. Hazard Mater. 75 (2) (2000) 165-180.   DOI
7 K. Clark, S. Bousquet, Irma's Here. But if You're Still Leaving by Car, This Is what Traffic Is like, The Miami Herald, 2017.
8 X. Chen, J.W. Meaker, F.B. Zhan, Agent-based modeling and analysis of hurricane evacuation procedures for the Florida Keys, Nat. Hazards 38 (3) (2006) 321.   DOI
9 J.-B. Chung, E.-S. Kim, Public perception of energy transition in Korea: nuclear power, climate change, and party preference, Energy Pol. 116 (2018) 137-144.   DOI
10 N. Golshani, et al., Evacuation decision behavior for no-notice emergency events, Transport. Res. Transport Environ. 77 (2019) 364-377.   DOI
11 S. Liu, P. Murray-Tuite, L. Schweitzer, Analysis of child pick-up during daily routines and for daytime no-notice evacuations, Transport. Res. Pol. Pract. 46 (1) (2012) 48-67.   DOI
12 IAEA, Power Reactor Information System, 2018 [cited 2020 01.02]; Available from, https://pris.iaea.org/PRIS/CountryStatistics/CountryDetails.aspx?current=KR.
13 W. Yin, et al., An agent-based modeling system for travel demand simulation for hurricane evacuation, Transport. Res. C Emerg. Technol. 42 (2014) 44-59.   DOI
14 U. Petruccelli, Urban evacuation in seismic emergency conditions, ITE J. 73 (8) (2003) 34-38.
15 C. Domonoske, Why Didn't Officials Order the Evacuation of Houston, National Public Radio, 2017.
16 C. Bulumulla, et al., The importance of modelling realistic human behaviour when planning evacuation schedules, in: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, 2017.
17 E. Harten, et al., Evaluation of traffic mitigation strategies for pre-hurricane emergency evacuations, in: Systems and Information Engineering Design Symposium (SIEDS), 2018, IEEE, 2018.
18 H. Fu, C.G. Wilmot, Sequential logit dynamic travel demand model for hurricane evacuation, Transport. Res. Rec. 1882 (1) (2004) 19-26.   DOI
19 J. Lee, et al., The estimated evacuation time for the emergency planning zone of the Kori nuclear site, with a focus on the precautionary action zone, J. Radiat. Protect. Res. 41 (3) (2016) 196-205.   DOI
20 E. Stern, Z. Sinuany-Stern, A behavioural-based simulation model for urban evacuation, Pap. Reg. Sci. 66 (1) (1989) 87-103.   DOI
21 J.P. van der Gun, A.J. Pel, B. van Arem, A general activity-based methodology for simulating multimodal transportation networks during emergencies, Eur. J. Transport Infrastruct. Res. 16 (3) (2016).
22 X. Chen, F.B. Zhan, Agent-based modelling and simulation of urban evacuation: relative effectiveness of simultaneous and staged evacuation strategies, J. Oper. Res. Soc. 59 (1) (2008) 25-33.   DOI
23 N.E. Lownes, R.B. Machemehl, Sensitivity of simulated capacity to modification of VISSIM driver behavior parameters, Transport. Res. Rec. 1988 (1) (2006) 102-110.   DOI
24 A.J. Pel, S.P. Hoogendoorn, M.C. Bliemer, Evacuation modeling including traveler information and compliance behavior, Procedia Eng. 3 (2010) 101-111.   DOI
25 T. Schwanen, D. Ettema, Coping with unreliable transportation when collecting children: examining parents' behavior with cumulative prospect theory, Transport. Res. Pol. Pract. 43 (5) (2009) 511-525.   DOI
26 C.R. Bhat, S.K. Singh, A comprehensive daily activity-travel generation model system for workers, Transport. Res. Pol. Pract. 34 (1) (2000) 1-22.   DOI
27 S. Park, S. Sohn, M. Jae, Cohort-based Evacuation Time Estimation Using TSISCORSIM, Nuclear Engineering and Technology, 2020.
28 M.K. Lindell, C.S. Prater, Critical behavioral assumptions in evacuation time estimate analysis for private vehicles: examples from hurricane research and planning, J. Urban Plann. Dev. 133 (1) (2007) 18-29.   DOI
29 R. Fries, Y. Qi, S. Leight, How Many Times Should I Run the Model? Performance Measure Specific Findings from VISSIM Models in Missouri, 2017.
30 U.S. Nuclear Regulatory Commission, Criteria for Development of Evacuation Time Estimate Studies, in NUREG/CR-7002/SAND2010-0016P, 2011.
31 D. Helbing, I. Farkas, T. Vicsek, Simulating dynamical features of escape panic, Nature 407 (6803) (2000) 487.   DOI
32 P.M. Murray-Tuite, H.S. Mahmassani, Model of household trip-chain sequencing in emergency evacuation, Transport. Res. Rec. 1831 (1) (2003) 21-29.   DOI
33 T.E. Drabek, Human System Responses to Disaster: an Inventory of Sociological Findings, Springer Science & Business Media, 2012.
34 Ulsan Metropolitan Office of Education, Current Status of Seosaeng Elementary School, 2018. Available from, https://school.use.go.kr/seosaeng-e/M010205/.
35 Greenpeace, It Is a Dangerous Decision to Cancel the Construction Permit of Shin-Kori Units 5 and 6, Greenpeace, 2016.
36 P. Hidas, A functional evaluation of the AIMSUN, PARAMICS and VISSIM microsimulation models, Road Transp. Res. 14 (4) (2005) 45.
37 PTV, A., PTV Vissim 10 User Manual. PTV AG, Karlsruhe, Germany, 2018.
38 Ulsan Metropolitan Office of Education, Current Status of Seosaeng Middle School, 2018. Available from, https://school.use.go.kr/seosaeng-m/M010208/.
39 T.J. Cova, J.P. Johnson, Microsimulation of neighborhood evacuations in the Urban-wildland interface, Environ. Plann. 34 (12) (2002) 2211-2229.   DOI
40 R.B. Goldblatt, K. Weinisch, Evacuation Planning, Human Factors, and Traffic Engineering: Developing Systems for Training and Effective Response, TR news, 2005, 238.