Exploring the Impact of Driver Sex, Driver Age, Area Type, and Lighting Conditions on Rear-End Collision Severity

Details

• Creators:
  Naseralavi, Saber ; Soltanirad, Mohammad ; Baghersad, Mahdi ; Ranjbar, Erfan ; Jimee, Keshav ; Mazaheri, Akram
• Corporate Creators:
  University of Kerman, Kerman, Iran ; Texas Tech University. Department of Civil, Environmental, and Construction Engineering ; University of Alabama at Birmingham. Dept. of Civil, Construction, and Environmental Engineering ; University of Delaware. Department of Civil and Environmental Engineering ; Tarbiat Modares University. Tehran, Iran
• Subject/TRT Terms:
  Crash Severity Highway Safety Information System (HSIS) Logistic Regression Analysis Rear End Collisions Traffic Safety
• DOI:
  https://doi.org/10.82042/crpase.11.3.2957
• Resource Type:
  Journal Article
• Right Statement:
  Open Access; https://creativecommons.org/licenses/by/4.0/
• Geographical Coverage:
  United States
• Corporate Publisher:
  Computational Research Progress in Applied Science & Engineering (CRPASE)
• Abstract:
  This study investigates the factors influencing injury severity in rear-end collisions in California using data from the Highway Safety Information System (HSIS). A total of 569,386 rear-end crashes recorded between 2015 and 2017 are analyzed. The dataset is divided into 24 subgroups based on driver sex (male, female), driver age (under 25, 25–65, over 65), area type (urban, rural), and lighting conditions (daylight, dark). For each subgroup, a binary logistic regression model is developed to examine the likelihood of injury (injury vs. no injury). Results indicate that crash severity is influenced by the number of vehicles, AADT, access control, surface type, terrain, lighting, time of crash, vehicle year, and season. Among all predictors, crashes involving three or more vehicles are consistently linked to lower injury odds, while high traffic volume and full freeway access control increase severity. Model performance is assessed using goodness-of-fit and discrimination measures, including deviance, AIC, pseudo R², and AUC. Of the 24 models, the best performance is observed for older female drivers in rural dark conditions, while the poorest is for young male drivers in rural dark conditions. These findings underscore the value of disaggregated modeling and suggest that traffic safety interventions can be tailored to specific demographic and environmental contexts.
• Content Notes:
  This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license https://creativecommons.org/licenses/by/4.0/. Please cite this article as: S. Naseralavi, M. Soltanirad, M. Baghersad, E. Ranjbar, K. Jimee, A. Mazaheri, Exploring the Impact of Driver Sex, Driver Age, Area Type, and Lighting Conditions on Rear-End Collision Severity, Computational Research Progress in Applied Science & Engineering, CRPASE: Transactions of Civil and Environmental Engineering 11 (2025) 1–16, Article ID: 2957. https://doi.org/10.82042/crpase.11.3.2957
• Format:
  PDF
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:94678fe6e82ac8a0b73c869e9e9b6d268c0f486e2212d6a4f171eb49f6fd2df4d59754bc98a8016cbeb1f221d3635126ff63f5e23a5001abe2910e3f4642d6ff
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/86537/dot_86537_DS1.pdf
• File Type:
  [PDF - 626.18 KB ]