Modeling pre-evacuation delay by evacuees in World Trade Center Towers 1 and 2 on September 11, 2001: A revisit using regression analysis

M. F. Sherman, M. Peyrot, L. A. Magda, R. R.M. Gershon

Research output: Contribution to journalArticle

Abstract

We have tested a linear regression model to identify significant predictors of pre-evacuation delay in a sample of evacuees enrolled in the World Trade Center Evacuation Study. We have found that pre-evacuation delay was greater when there were more environmental cues, more seeking out of information, and more pre-evacuation actions. Additionally, higher perceived risk was predictive of shorter pre-evacuation delay times. These findings are compared and contrasted with an analysis of participants in the National Institute of Standards and Technology investigation of the World Trade Center disaster, recently reported by Kuligowski and Mileti (2009). Both studies reported factors associated with pre-evacuation delay that were similar to those associated with community evacuation. Additionally, we found that greater knowledge and greater emergency preparedness were associated with greater perception of risk. Greater emergency preparedness was negatively related to pre-evacuation delay within World Trade Center Tower I, but within World Trade Center Tower II, the relation between emergency preparedness and pre-evacuation delay was positive. These findings have implications for training of occupants of high-rise buildings.

Original languageEnglish (US)
Pages (from-to)414-424
Number of pages11
JournalFire Safety Journal
Volume46
Issue number7
DOIs
StatePublished - Oct 2011

Keywords

  • Decision making
  • Evacuation
  • Predictors of pre-evacuation delay
  • World Trade Center

ASJC Scopus subject areas

  • Chemistry(all)
  • Materials Science(all)
  • Safety, Risk, Reliability and Quality
  • Physics and Astronomy(all)

Fingerprint Dive into the research topics of 'Modeling pre-evacuation delay by evacuees in World Trade Center Towers 1 and 2 on September 11, 2001: A revisit using regression analysis'. Together they form a unique fingerprint.

  • Cite this