Using Latent Class Analysis and Mixed Logit Model to Explore Risk Factors on Driver Injury Severity in Single-Vehicle Crashes

The single-vehicle crash has been recognized as a critical crash type due to its high fatality rate. In this study, a two-year crash dataset including all single-vehicle crashes in New Mexico is adopted to analyze the impact of contributing factors on driver injury severity. In order to capture the across-class heterogeneous effects, a latent class approach is designed to classify the whole dataset by maximizing the homogeneous effects within each cluster. The mixed logit model is subsequently developed on each cluster to account for the within-class unobserved heterogeneity and to further analyze the dataset. According to the estimation results, several variables including overturn, fixed object, and snowing, are found to be normally distributed in the observations in the overall sample, indicating there exist some heterogeneous effects in the dataset. Some fixed parameters, including rural, wet, overtaking, seatbelt used, 65 years old or older, etc., are also found to significantly influence driver injury severity. This study provides an insightful understanding of the impacts of these variables on driver injury severity in single-vehicle crashes, and a beneficial reference for developing effective countermeasures and strategies for mitigating driver injury severity.

• Record URL:
  https://doi.org/10.1016/j.aap.2019.04.001
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0001457519305160
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Li, Zhenning
  • 0000-0002-0877-6829
  • Wu, Qiong
  • Ci, Yusheng
  • Chen, Cong
  • 0000-0001-5928-3277
  • Chen, Xiaofeng
  • Zhang, Guohui
  • 0000-0001-5194-9222
• Publication Date: 2019-8

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 230-240
• Serial:
  • Accident Analysis & Prevention
  • Volume: 129
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Crash causes; Crash data; Crash injuries; Injury severity; Risk assessment; Single vehicle crashes
• Geographic Terms: New Mexico
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01711123
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 17 2019 11:36AM