Driving Risk Evaluation Based on Multidimensional Data

This paper proposes a crash-involvement risk evaluation method using multidimensional data including drivers’ license and violation records, vehicle registration information, camera data at intersections, and traffic crash data. A bagging-support vector machine (Bagging-SVM) model is trained using both static and behavioral features to evaluate a driver’s crash-involvement risk. A feature selection method based on the F-score is performed before the risk evaluation. The results show that this extraction method can improve the stability of the evaluation, and provides the importance of each feature. A case study on truck drivers from a medium-size city in northern China is presented. It is found that driving behavior such as violations, trips at midnight, and headways are significantly related to crash-involvement.

• Record URL:
  https://doi.org/10.1061/9780784481523.194
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784481523
• Supplemental Notes:
  • © 2018 American Society of Civil Engineers 2018
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Gui, Zixuan
  • Chen, Hongxin
  • Yang, Zi
  • Pei, Xin
  • Zhang, Zuo
• Conference:
  • 18th COTA International Conference of Transportation Professionals
  • Location: Beijing , China
  • Date: 2018-7-5 to 2018-7-8
• Publication Date: 2018-7

Language

• English

Media Info

• Media Type: Web
• Pagination: pp 1957-1966
• Monograph Title: CICTP 2018: Intelligence, Connectivity, and Mobility

Subject/Index Terms

• TRT Terms: Crash data; Driving behavior; Risk assessment; Traffic crashes; Traffic data; Truck drivers
• Geographic Terms: China
• Subject Areas: Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01870134
• Record Type: Publication
• ISBN: 9780784481523
• Files: TRIS, ASCE
• Created Date: Jan 19 2023 11:23AM