Hybrid Intelligent Technologies Based Safety Region Estimation for Real-Time Crash Risk Evaluation Application

This paper first introduces the concept of traffic safety region to real-time crash risk evaluation. A hybrid intelligent algorithm, combining sequential forward selection (SFS), principal components analysis (PCA) and least squares support vector machines (LSSVM), is presented to estimate traffic safety region and classify the traffic safety states. Based on the estimated traffic safety region, safety margin is calculated to measure the traffic crash risk in real time. To demonstrate the advantage of the proposed method, this paper develops two crash risk evaluation models, namely SFS-LSSVM model and PCA-LSSVM model, based on crash data and non-crash data collected on freeway I-880N in Alameda. Validation results show that the method is of reasonably high accuracy for identifying traffic safety states, and then the safety margin is a meaningful indicator for real-time crash risk evaluation.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Yang, Yanfang
  • Zhang, Qing
  • Qin, Yong
  • Ma, Xiaoping
  • Dong, Honghui
  • Jia, Limin
• 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: 17p
• Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Classification; Crash risk forecasting; Reliability; Traffic safety
• Identifier Terms: Interstate 880
• Uncontrolled Terms: Principal component analysis
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01622577
• Record Type: Publication
• Report/Paper Numbers: 17-02829
• Files: TRIS, TRB, ATRI
• Created Date: Jan 17 2017 9:47AM