Skip to main content
Article
Freeway Work-Zone Crash Analysis and Risk Identification Using Multiple and Conditional Logistic Regression
Journal Of Transportation Engineering
  • Rami Harb, University of Central Florida
  • Essam Radwan, University of Central Florida
  • Xuedong Yan, University of Central Florida
  • Anurag Pande, University of Central Florida
  • Mohamed Abdel-Aty, University of Central Florida
Publication Date
5-1-2008
Abstract

Work-zone safety continues to be a priority and a concern for the Federal Highway Association as well as most state departments of transportation. The main objective of this study is to uncover work-zone freeway crash characteristics to help develop countermeasures that limit work-zones’ hazards. The Florida Crash Records Database for years 2002, 2003, and 2004 was utilized for this study. Conditional logistic regression along with stratified sampling and multiple logistic regression models were estimated to unveil work-zone freeway crash traits. According to the models’ results, roadway geometry, weather condition, age, gender, lighting condition, residence code, and driving under the influence of alcohol and/or drugs are significant risk factors associated with work-zone crashes.

Citation Information
Rami Harb, Essam Radwan, Xuedong Yan, Anurag Pande, et al.. "Freeway Work-Zone Crash Analysis and Risk Identification Using Multiple and Conditional Logistic Regression" Journal Of Transportation Engineering Vol. 134 Iss. 5 (2008) p. 203 - 214
Available at: http://works.bepress.com/apande/15/