IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v13y2016i10p986-d80126.html
   My bibliography  Save this article

A Method for Formulizing Disaster Evacuation Demand Curves Based on SI Model

Author

Listed:
  • Yulei Song

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Xuedong Yan

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

Abstract

The prediction of evacuation demand curves is a crucial step in the disaster evacuation plan making, which directly affects the performance of the disaster evacuation. In this paper, we discuss the factors influencing individual evacuation decision making (whether and when to leave) and summarize them into four kinds: individual characteristics, social influence, geographic location, and warning degree. In the view of social contagion of decision making, a method based on Susceptible-Infective (SI) model is proposed to formulize the disaster evacuation demand curves to address both social influence and other factors’ effects. The disaster event of the “Tianjin Explosions” is used as a case study to illustrate the modeling results influenced by the four factors and perform the sensitivity analyses of the key parameters of the model. Some interesting phenomena are found and discussed, which is meaningful for authorities to make specific evacuation plans. For example, due to the lower social influence in isolated communities, extra actions might be taken to accelerate evacuation process in those communities.

Suggested Citation

  • Yulei Song & Xuedong Yan, 2016. "A Method for Formulizing Disaster Evacuation Demand Curves Based on SI Model," IJERPH, MDPI, vol. 13(10), pages 1-21, October.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:10:p:986-:d:80126
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/13/10/986/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/13/10/986/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    2. Elaine Vaughan, 1995. "The Significance of Socioeconomic and Ethnic Diversity for the Risk Communication Process," Risk Analysis, John Wiley & Sons, vol. 15(2), pages 169-180, April.
    3. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    4. Hasan, Samiul & Ukkusuri, Satish V., 2011. "A threshold model of social contagion process for evacuation decision making," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1590-1605.
    5. Lindell, Michael K., 2008. "EMBLEM2: An empirically based large scale evacuation time estimate model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(1), pages 140-154, January.
    6. David Hirshleifer & Siew Hong Teoh, 2003. "Herd Behaviour and Cascading in Capital Markets: a Review and Synthesis," European Financial Management, European Financial Management Association, vol. 9(1), pages 25-66, March.
    7. Michael K. Lindell & Ronald W. Perry, 2012. "The Protective Action Decision Model: Theoretical Modifications and Additional Evidence," Risk Analysis, John Wiley & Sons, vol. 32(4), pages 616-632, April.
    8. Mustafa Anil Yazici & Kaan Ozbay, 2008. "Evacuation Modelling in the United States: Does the Demand Model Choice Matter?," Transport Reviews, Taylor & Francis Journals, vol. 28(6), pages 757-779, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiaoran Liu & Luemiao Zhang & Jiliang Zhen & Wei Wang, 2024. "Planning for service space of medium- and long-term shelters based on multi-agent evacuation simulation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(14), pages 12769-12796, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pegah Dehghani & Ros Zam Zam Sapian, 2014. "Sectoral herding behavior in the aftermarket of Malaysian IPOs," Venture Capital, Taylor & Francis Journals, vol. 16(3), pages 227-246, July.
    2. Feri, Francesco & Meléndez-Jiménez, Miguel A. & Ponti, Giovanni & Vega-Redondo, Fernando, 2011. "Error cascades in observational learning: An experiment on the Chinos game," Games and Economic Behavior, Elsevier, vol. 73(1), pages 136-146, September.
    3. Jonathan E. Alevy & Michael S. Haigh & John List, 2006. "Information Cascades: Evidence from An Experiment with Financial Market Professionals," NBER Working Papers 12767, National Bureau of Economic Research, Inc.
    4. Louise Allsopp, 2004. "An Experiment to Investigate the Externalities of Search," The Economic Record, The Economic Society of Australia, vol. 80(251), pages 423-435, December.
    5. Gavriilidis, Konstantinos & Kallinterakis, Vasileios & Montone, Maurizio, 2024. "Political uncertainty and institutional herding," Journal of Corporate Finance, Elsevier, vol. 88(C).
    6. Hott, Christian, 2009. "Herding behavior in asset markets," Journal of Financial Stability, Elsevier, vol. 5(1), pages 35-56, January.
    7. Kapetanios, George & Mitchell, James & Shin, Yongcheol, 2014. "A nonlinear panel data model of cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 179(2), pages 134-157.
    8. Lubomír Cingl, 2018. "Social learning under acute stress," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-26, August.
    9. Cai, Fang & Han, Song & Li, Dan & Li, Yi, 2019. "Institutional herding and its price impact: Evidence from the corporate bond market," Journal of Financial Economics, Elsevier, vol. 131(1), pages 139-167.
    10. Antonio Guarino & Steffen Huck & Heike Harmgart, 2008. "When half the truth is better than the truth: A Theory of aggregate information cascades," WEF Working Papers 0046, ESRC World Economy and Finance Research Programme, Birkbeck, University of London.
    11. Vasileios Kallinterakis & Nomana Munir & Mirjana Radovic-Markovic, 2010. "Herd Behaviour, Illiquidity and Extreme Market States," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 9(3), pages 305-324, December.
    12. Antonio Guarino & Philippe Jehiel, 2013. "Social Learning with Coarse Inference," American Economic Journal: Microeconomics, American Economic Association, vol. 5(1), pages 147-174, February.
    13. Wen-Lin Wu & Yin-Feng Gau, 2017. "Home bias in portfolio choices: social learning among partially informed agents," Review of Quantitative Finance and Accounting, Springer, vol. 48(2), pages 527-556, February.
    14. Andreas Roider & Andrea Voskort, 2016. "Reputational Herding in Financial Markets: A Laboratory Experiment," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 17(3), pages 244-266, July.
    15. Gavriilidis, Konstantinos & Kallinterakis, Vasileios & Tsalavoutas, Ioannis, 2016. "Investor mood, herding and the Ramadan effect," Journal of Economic Behavior & Organization, Elsevier, vol. 132(S), pages 23-38.
    16. Camara, Omar, 2017. "Industry herd behaviour in financing decision making," Journal of Economics and Business, Elsevier, vol. 94(C), pages 32-42.
    17. Vibha Gaba & Ann Terlaak, 2013. "Decomposing Uncertainty and Its Effects on Imitation in Firm Exit Decisions," Organization Science, INFORMS, vol. 24(6), pages 1847-1869, December.
    18. Sushil Bikhchandani & David Hirshleifer & Omer Tamuz & Ivo Welch, 2024. "Information Cascades and Social Learning," Journal of Economic Literature, American Economic Association, vol. 62(3), pages 1040-1093, September.
    19. Caglayan, Mustafa & Talavera, Oleksandr & Zhang, Wei, 2021. "Herding behaviour in P2P lending markets," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 27-41.
    20. M. Fern'andez-Mart'inez & M. A S'anchez-Granero & Mar'ia Jos'e Mu~noz Torrecillas & Bill McKelvey, 2016. "A comparison among some Hurst exponent approaches to predict nascent bubbles in $500$ company stocks," Papers 1601.04188, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:13:y:2016:i:10:p:986-:d:80126. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.