Context-aware driver risk prediction with telematics data

Driving risk prediction is crucial for safety and risk mitigation. While traditional methods rely on demographic information for insurance pricing, they may not fully capture actual driving behavior. To address this, telematics data has gained popularity. This study focuses on using telematics data and contextual information (e.g., road type, daylight) to represent a driver’s style through tensor representations. Drivers with similar behaviors are identified by clustering their representations, forming risk cohorts. Past at-fault traffic accidents and citations serve as partial risk labels. The relative magnitude of average records (per driver) for each cohort indicates their risk label, such as low or high risk, which can be transferred to drivers in a cohort. A classifier is then constructed using augmented risk labels and driving style representations to predict driving risk for new drivers. Real-world data from major US cities validates the effectiveness of this framework. The approach is practical for large-scale scenarios as the data can be obtained at scale. Its focus on driver-based risk prediction makes it applicable to industries like auto-insurance. Beyond personalized premiums, the framework empowers drivers to assess their driving behavior in various contexts, facilitating skill improvement over time.

• Record URL:
  • https://doi.org/10.1016/j.aap.2023.107269
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0001457523003160
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Moosavi, Sobhan
  • 0000-0002-2368-4498
  • Ramnath, Rajiv
• Publication Date: 2023-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 107269
• Serial:
  • Accident Analysis & Prevention
  • Volume: 192
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Data analysis; Driver performance; High risk drivers; Predictive models; Risk taking; Telematics
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01895324
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 5 2023 9:31AM