Investigating personalized exit choice behavior in fire accidents using the hierarchical Bayes estimator of the random coefficient logit model

• Published in: Analytic methods in accident research 2021-03, Vol.29, p.100140, Article 100140
• Main Authors: Song, Xiang Ben, Lovreglio, Ruggiero
• Format: Article
• Language: English
• Subjects: Evacuation modeling; Exit choice; Hierarchical Bayes; Random coefficient logit
• ISSN: 2213-6657; 2213-6657
• DOI: 10.1016/j.amar.2020.100140

Understanding how people behave during the fire evacuation is fundamental to predict the required time to evacuate buildings and transportation systems and enhance their safety. To date, several studies have been carried out to investigate the impact of several social and environmental factors on how evacuees choose exits while moving towards safe places. However, none of the existing studies has identified individual-specific taste preferences for exit choice behavior. As such, they do not allow the analyses of individual-specific preferences across heterogeneous decision makers. This work overcomes this limitation through a Hierarchical Bayes estimation approach, which allows the estimation of individual-specific parameters. The methodology is applied to the data of non-immersive Virtual Reality-based stated preference experiments. With the individual-level knowledge, analyses show more granular insights of decision makers’ trade-off of different factors, including two distinct groups of people as “followers” or “non-followers” and the impact of age, nationality, and education on the herding behavior during the fire evacuation.