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Title: A decision model for pre-evacuation time prediction based on fuzzy logic theory
Authors: Wang, Ke
Qian, Shunzhi
Fu, Zhijian
Keywords: Engineering::Civil engineering
Issue Date: 2020
Source: Wang, K., Qian, S. & Fu, Z. (2020). A decision model for pre-evacuation time prediction based on fuzzy logic theory. 13th Traffic and Granular Flow (TGF) Conference, 252, 265-274.
metadata.dc.contributor.conference: 13th Traffic and Granular Flow (TGF) Conference
Abstract: Efficient evacuation is crucial for reducing deaths and injuries caused by disastrous events such as earthquakes. Notably, pre-evacuation time constitutes a large proportion of the total evacuation time; whether and when to initiate the evacuation largely determines the outcome of the evacuation in an emergency. Despite considerable efforts made to elaborate the pre-evacuation process, the evacuees’ vague and imprecise cognitive evaluation on the environment in pre-evacuation decision-making process is underrepresented in these studies. This study aims to enrich behavioral knowledge in the evacuation process during earthquakes and to explore modeling methods for characterization of the pre-evacuation process. As such, we conducted detailed analysis of real earthquake evacuation records to gain some insight into evacuees’ behavioral features. The extracted information from the records, together with the empirical knowledge formed the basis of building a fuzzy logic based decision-making model. The proposed model allowed the prediction of investigating/evacuating decision time with the consideration of individual heterogeneity and changes of cues. The validity of this model was validated against real-case data with reasonable agreement in average pre-evacuation time. A further parametric study was conducted to investigate the influence of features of physical signals and those of instructions on the investigating/evacuating decisions.
ISBN: 9783030559724
DOI: 10.1007/978-3-030-55973-1_33
Schools: School of Civil and Environmental Engineering 
Rights: © 2020 Springer Nature Switzerland AG. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Conference Papers

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