Identifying Significant Injury Severity Risk Factors in Traffic Accidents Based on the Machine Learning Methods

Traffic safety is one of the crucial problems in many countries. Understanding the conditions under which people are more likely to be killed or more severely injured in traffic accidents, can improve the overall driving safety level. Factors that affect the risk of increased injury of occupants in an automotive accident include characteristics of the person, environmental factors, and roadway conditions at the time of the accident, technical characteristics of the vehicle itself, among others. In this study, the authors used a large crash data set along with several machine learning methods to model the complex relationships between the number of crashes that correspond to different injury severity levels and the crash related risk factors. Sensitivity analysis is conducted on the trained predictive models to identify the prioritized importance of crash-related factors. The results expose the relative importance of crash related risk factors with the changing levels of injury severity.

• Record URL:
  https://doi.org/10.1061/9780784482292.326
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784482292
• Supplemental Notes:
  • © 2019 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Zhang, Wei
  • Zhou, Zhuping
  • Li, Lei
  • Huang, Rui
• Conference:
  • 19th COTA International Conference of Transportation Professionals
  • Location: Nanjing , China
  • Date: 2019-7-6 to 2019-7-8
• Publication Date: 2019-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Monograph Title: CICTP 2019: Transportation in China—Connecting the World

Subject/Index Terms

• TRT Terms: Artificial intelligence; Crash causes; Crash data; Crash injuries; Crash risk forecasting; Highway safety; Injury severity; Machine learning; Traffic crashes
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01714464
• Record Type: Publication
• ISBN: 9780784482292
• Files: TRIS, ASCE
• Created Date: Aug 22 2019 4:41PM