Ahmadi, A., Jahangiri, A., Berardi, V. and Machiani, S. (2020) Crash Severity Analysis of Rear-End Crashes in California Using Statistical and Machine Learning Classification Methods. Journal of Transportation Safety & Security, 12, 522-546.
https://doi.org/10.1080/19439962.2018.1505793

has been cited by the following article:

• TITLE: Examining the Risk Factors of Rear-End Crashes at Signalized Intersections

  AUTHORS: Mostafa Sharafeldin, Ahmed Farid, Khaled Ksaibati

  KEYWORDS: Rear-End Crashes, Signalized Intersections, Pavement Friction, Intersection Safety, Binomial Logistic Regression

  JOURNAL NAME: Journal of Transportation Technologies, Vol.12 No.4, September 8, 2022

  ABSTRACT: Rear-end crashes are among the most common crash types at signalized intersections. To examine the risk factors for the occurrence of this crash type, this study involved the analysis of nine years of intersection crash records in the state of Wyoming. With that, the contributing factors related to crash, driver, environmental, and roadway characteristics, including pavement surface friction, were investigated. A binomial logistic regression modeling approach was applied to achieve the study’s objective. The results showed that three factors related to crash and driver’s attributes (commercial vehicle involvement, speeding, and driver’s age) and four factors related to environmental and roadway characteristics (lighting, weather conditions, area type, whether urban or rural and pavement friction) are associated with the risk of rear-end crash occurrence at signalized intersections. This study provides insights into the mitigation measures to implement concerning rear-end crashes at signalized intersections.