A data-driven Bayesian network for probabilistic crash risk assessment of individual driver with traffic violation and crash records

In recent years, individual drivers‘ crash risk assessments have received much attention for identifying high-risk drivers. To this end, the authors propose a probabilistic assessment method of crash risks with a reproducible long-term dataset (i.e., traffic violations, license, and crash records). In developing this method, the authors used 7.75 million violations and crashes of 5.5 million individual drivers in Seoul, South Korea, from June 2013 to June 2017 (four years). The stochastic process of the Bayesian network (BN), whose structure is optimized by tabu-search, successfully evaluates individual drivers' crash and violation probability. In addition, the cluster analysis classifies drivers into five distinctive groups according to their estimated violation and crash probabilities. As a result, this study found that the estimated average crash rate within a cluster converges with the actual crash rate by the proposed framework without privacy issues. The authors also confirm that violation records and expected crash probability are strongly correlated, and there is a direct relationship between a driver’s previous violations and crash record and the future at-fault crash. The proposed assessment method is valuable in developing proactive driver education programs and safety countermeasures, including adjusting the penalty system and developing user-based insurance by recognizing dangerous drivers and identifying their properties.

• Record URL:
  • https://doi.org/10.1016/j.aap.2022.106790
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0001457522002251
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Joo, Yang-Jun
  • Kho, Seung-Young
  • Kim, Dong-Kyu
  • Park, Ho-Chul
• Publication Date: 2022-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 106790
• Serial:
  • Accident Analysis & Prevention
  • Volume: 176
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Crash causes; High risk drivers; Moving violations; Risk assessment
• Geographic Terms: Seoul (Korea)
• Subject Areas: Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01856832
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 30 2022 5:27PM