A Model of Injury Severity Prediction in Traffic Accident Based on GA-BP Neural Network

Understanding the non-linear relationship between traffic injury severity and factors in accuracy can help decrease accident occurrence and improve driving safety. This paper uses a GA-BP neural network to model the relationship and predict injury severity in traffic accidents classified into fatality, serious crash, and slight crash. And it validates the superior performance of GA-BP with crash data from the UK in 2015, compared to the BP neural network and the logistic regression model. A sensitivity analysis is applied to find out the contribution that input variables have on injury severity. This paper indicates that the GA-BP neural network provides a reference for injury severity prediction in traffic accident.

• Record URL:
  • https://doi.org/10.1061/9780784482292.215
• 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:
  • Wang, Shuang
  • Wei, Chong
  • Wei, Yansha
  • Wang, Wenzhe
  • Wu, Fei
• 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
• Monograph Title: CICTP 2019: Transportation in China—Connecting the World

Subject/Index Terms

• TRT Terms: Crash data; Crash injuries; Crash severity; Injury severity; Mathematical prediction
• Geographic Terms: United Kingdom
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01712655
• Record Type: Publication
• ISBN: 9780784482292
• Files: TRIS, ASCE
• Created Date: Jul 26 2019 1:21PM