A Novel Feature Extraction Model for Traffic Injury Severity and Its Application to FARS Data Analysis

The prevention of severe injuries during crashes has become one of the leading issues in traffic management and transportation safety. Identifying the impact factors which significantly affect traffic injury severity is critical for reducing the occurrence of severe injuries. In this study, the Fatality Analysis Reporting System (FARS) data from 2010 to 2014 was selected as the datasets for analysis. An algorithm named Improved Markov Blanket (IAMB) was proposed to extract the significant and common factors which affect crash injury severity from 29 variables related to driver characteristics, vehicle characteristics, accidents types, road condition, and environment characteristics. The Pearson correlation coefficient test is applied to verify the significant correlation between the selected factors and traffic injury severity. Two widely used classification algorithms (Bayesian networks and C4.5 decision tree) were employed to evaluate the performance of the proposed feature selection algorithm. The calculation result of the correlation coefficient, accuracy of classification, and classification error rate indicated that the IAMB not only could extract the significant impact factors, but also could improve the accuracy of classification. Meanwhile, the relationship between five selected factors (atmospheric condition, time of crash, alcohol test result, crash type, and driver distracted) and traffic injury severity was also analyzed in this study. The results indicated that crashes occurred in bad weather condition (e.g. foggy or worse), during night time, in drunk driving, in crash type of single driver, and in distracted driving are associated with more severe injuries.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ANB20 Standing Committee on Safety Data, Analysis and Evaluation.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Yan, Lixin
  • He, Yi
  • Qin, Lingqiao
  • Wu, Chaozhong
  • Zhu, Dunyao
  • Ran, Bin
• Conference:
  • Transportation Research Board 96th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2017-1-8 to 2017-1-12
• Date: 2017

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 16p
• Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Accuracy; Algorithms; Classification; Correlation analysis; Crash causes; Data analysis; Injury severity; Markov processes
• Identifier Terms: Fatality Analysis Reporting System
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01630608
• Record Type: Publication
• Report/Paper Numbers: 17-02777
• Files: TRIS, TRB, ATRI
• Created Date: Mar 28 2017 5:07PM