Song Yulei, Yan Xuedong
MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
Int J Environ Res Public Health. 2016 Oct 10;13(10):986. doi: 10.3390/ijerph13100986.
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.
疏散需求曲线的预测是灾难疏散计划制定中的关键步骤,它直接影响灾难疏散的效果。在本文中,我们讨论了影响个体疏散决策(是否以及何时离开)的因素,并将其归纳为四类:个体特征、社会影响、地理位置和预警程度。从决策的社会传染角度出发,提出了一种基于易感-感染(SI)模型的方法来构建灾难疏散需求曲线,以解决社会影响和其他因素的作用。以“天津爆炸”灾难事件为例,说明这四个因素对建模结果的影响,并对模型的关键参数进行敏感性分析。发现并讨论了一些有趣的现象,这对当局制定具体的疏散计划具有重要意义。例如,由于孤立社区的社会影响较低,可能需要采取额外措施来加速这些社区的疏散进程。