Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom

Although driving behavior has been largely studied amongst private motor vehicles drivers, the literature addressing heavy goods vehicle (HGV) drivers is scarce. Identifying the existing groups of driving stereotypes and their proportions enables researchers, companies, and policy makers to establish group-specific strategies to improve safety and economy. In addition, insight into driving styles can help predict drivers’ reactions and therefore enable the modeling of interactions between vehicles and the possible obstacles encountered on a journey. Consequently, there are also contributions to the research and development of autonomous vehicles and smart roads. In this paper, the author's interest lies in investigating driving behavior within the HGV community in the United Kingdom (U.K.). The authors conduct analysis of a telematics dataset containing the incident information on 21 193 HGV drivers across the U.K. The authors are interested in answering two research questions: 1) What groups of behavior are the authors able to uncover? 2) How do these groups complement current findings in the literature? To answer these questions, they apply a two-stage data analysis methodology involving consensus clustering and ensemble classification to the dataset. Through the analysis, eight patterns of behavior are uncovered. It is also observed that although the author's findings have similarities to those from previous work on driving behavior, further knowledge is obtained, such as extra patterns and driving traits arising from vehicle and road characteristics.

• Record URL:
  • https://doi.org/10.1109/TITS.2018.2875343
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2019, IEEE.
• Authors:
  • Figueredo, Grazziela P
  • Agrawal, Utkarsh
  • Mase, Jimiama M M
  • Mesgarpour, Mohammad
  • Wagner, Christian
  • Soria, Daniele
  • Garibaldi, Jonathan M
  • Siebers, Peer-Olaf
  • John, Robert I
• Publication Date: 2019-9

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 3324-3336
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 20
  • Issue Number: 9
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Algorithms; Behavior; Cluster analysis; Data analysis; Driver monitoring; Driver performance; Drivers; Telematics; Trucks
• Geographic Terms: United Kingdom
• Subject Areas: Finance; Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01718386
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Sep 27 2019 12:07PM