Studying the Role of Personality Traits on the Evacuation Choice Behavior Pattern in Urban Road Network in Different Severity Scales of Natural Disaster. (23rd November 2021)

• Record Type: Journal Article
• Authors: Mohajeri, Fatemeh; Mirbaha, Babak
• Other Names: Gupta Ankit Academic Editor.
• Is Part Of: Advances in civil engineering. Volume 2021(2021)
• Publication Date: 2021-11-23
• Subjects: Civil engineering -- Periodicals; Civil engineering; Periodicals; 624
• DOI: 10.1155/2021/9174484
• Languages: English
• ISSNs: 1687-8086

Abstract

Abstract : The study of evacuation behavior in response to disaster is necessary for emergency traffic management. As decision-making is not exclusively dependent on observable variables, in this research, it is attempted to study the evacuation choice behavior pattern in emergency response to earthquake disaster by considering both physical and behavioral factors. Personality traits are measured as behavioral latent factors by confirmatory factor analysis (CFA) of the short form of NEO-Five-Factor Inventory (NEO-FFI). A revealed preference survey with more than 700 samples was conducted in Qazvin city (Iran) which was based on real-life earthquake experience and the stated preference survey was conducted for six designated scenarios with different severities and times of earthquakes. Analysis of evacuation behavior is conducted by 3 types of discrete choice models (traditional binary logit model (TBLM), hybrid binary logit model (HBLM), and random parameters/mixed binary logit model (MBLM)). First, TBLM is estimated to study the effect of observable variables on response of people to earthquake disaster. Then, by adding the personality traits to modeling structure and constructing HBLM, the correct prediction percentage of the model increased. This study also considers heterogeneous mixtures of population in terms of income, family size, and five factors of personality traits by MBLM. The MBLM captures the heterogeneous responses of the respondent. By considering these variables as random parameters, the Log Likelihood function and pseudo square ( ρ 2 ) of the model increased.