Development of injury prediction models for advanced automatic collision notification based on Japanese accident data

In this paper, injury prediction models for estimating serious injury risk of occupants were developed based on accident data available in the Japanese statistical DB. Four types of model by crash direction (frontal crash model, near-side crash model, far-side crash model, rear-end crash model) were developed. These models were developed by using a logistic regression modelling technique based on data from Japanese ITARDA (Institute for Traffic Accident Research and Data Analysis) police-reported statistics, a large database for the last decade. Risk factors of the model are delta-V, belt use, multiple impact crash and occupant's age. Serious injury risk for four crash directions was estimated by the model. A comparison has been done between estimated serious injury risk and actual injury of Japanese ITARDA in-depth accident data (micro-data). The results show that the injury prediction model has a possibility for predicting injury risk based on onboard data and its application for post-crash safety.

• Record URL:
  • https://doi.org/10.1080/13588265.2015.1132970
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13588265
• Supplemental Notes:
  • Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Yoshida, S
  • Hasegawa, T
  • Tominaga, S
  • Nishimoto, T
• Publication Date: 2016-3

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 112-119
• Serial:
  • International Journal of Crashworthiness
  • Volume: 21
  • Issue Number: 2
  • Publisher: Taylor & Francis
  • ISSN: 1358-8265
  • Serial URL: http://www.tandfonline.com/loi/tcrs20

Subject/Index Terms

• TRT Terms: Automatic crash notification; Crash data; Crash injuries; Fatalities; Injury severity; Logistic regression analysis; Traffic crashes; Vehicle occupants
• Geographic Terms: Japan
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01596717
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 22 2016 10:45AM