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Open AccessArticle

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

MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
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Academic Editor: Suren Chen
Int. J. Environ. Res. Public Health 2016, 13(10), 986; https://doi.org/10.3390/ijerph13100986
Received: 6 August 2016 / Revised: 15 September 2016 / Accepted: 27 September 2016 / Published: 10 October 2016
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
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. View Full-Text
Keywords: evacuation demand curves; social influence; Susceptible-Infective model; sensitivity analyses; Tianjin Explosions evacuation demand curves; social influence; Susceptible-Infective model; sensitivity analyses; Tianjin Explosions
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Song, Y.; Yan, X. A Method for Formulizing Disaster Evacuation Demand Curves Based on SI Model. Int. J. Environ. Res. Public Health 2016, 13, 986.

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