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.
Highlights
The past decades have witnessed an increasing number of disasters which have caused huge losses of life and property [1]
Some interesting phenomena are found and discussed, which is meaningful for authorities to make specific evacuation plans
This study aims to develop a model taking into account both social influence and other important factors like individual characteristics, location, and warning degree to formulize the disaster evacuation demand curves
Summary
The past decades have witnessed an increasing number of disasters which have caused huge losses of life and property [1]. The WenChuan earthquake killed around 70 thousand people and caused direct economic losses of 8451 billion Yuan. To prevent or at least minimize the losses, an efficient evacuation plan is regarded to be crucial [2]. In the whole process of evacuation planning, the evacuation demands estimation is considered a key step. Lacking an accurate prediction of the evacuation demands, large traffic congestions might happen in disaster evacuations [3,4]. The times at which people begin to evacuate can be expressed as an evacuation response curve or an evacuation demand curve [5,6,7]
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