Identifying and Labeling Potentially Risky Driving: A Multistage Process Using Real-World Driving Data

Every year, over 50 million people are injured and 1.35 million die in traffic accidents. Risky driving behaviors are responsible for over half of all fatal vehicle accidents. Identifying risky driving behaviors within real-world driving (RWD) datasets is a promising avenue to reduce the mortality burden associated with these unsafe behaviors, but numerous technical hurdles must be overcome to do so. Herein, the authors describe the implementation of a multistage process for classifying unlabeled RWD data as potentially risky or not. In the first stage, data are reformatted and reduced in preparation for classification. In the second stage, subsets of the reformatted data are labeled as potentially risky (or not) using the Iterative-DBSCAN method. In the third stage, the labeled subsets are then used to fit random forest (RF) classification models—RF models were chosen after they were found to be performing better than logistic regression and artificial neural network models. In the final stage, the RF models are used predictively to label the remaining RWD data as potentially risky (or not). The implementation of each stage is described and analyzed for the classification of RWD data from vehicles on public roads in Ann Arbor, Michigan. Overall, the authors identified 22.7 million observations of potentially risky driving out of 268.2 million observations. This study provides a novel approach for identifying potentially risky driving behaviors within RWD datasets. As such, this study represents an important step in the implementation of protocols designed to address and prevent the harms associated with risky driving.

• Record URL:
  https://doi.org/10.1155/2021/8819094
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/5121625
• Supplemental Notes:
  • © 2021 Charles Marks et al.
• Authors:
  • Marks, Charles
  • Jahangiri, Arash
  • Ghanipoor Machiani, Sahar
• Publication Date: 2021-2

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: Article ID 8819094
• Serial:
  • Journal of Advanced Transportation
  • Volume: 2021
  • Publisher: John Wiley & Sons, Incorporated
  • ISSN: 0197-6729
  • EISSN: 2042-3195
  • Serial URL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2042-3195
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Behavior; Data analysis; Drivers; Driving; High risk drivers; Highway safety; Risk analysis; Traffic crashes
• Geographic Terms: Ann Arbor (Michigan)
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01765332
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 22 2021 10:20AM