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.


  • English

Media Info

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

Subject/Index Terms

Filing Info

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