Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1318919
- Type
- D - Journal article
- DOI
-
10.1109/TITS.2018.2875343
- Title of journal
- IEEE Transactions on Intelligent Transportation Systems
- Article number
- -
- First page
- 3324
- Volume
- 20
- Issue
- 9
- ISSN
- 1524-9050
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
8
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper investigates driving behaviour within the Heavy Goods Vehicle community in the United Kingdom, using a telematics dataset of driver incident information to identify patterns of driving behaviour and driving traits arising from vehicle and road characteristics. Knowledge obtained was transferred to industry, and is now in use and increasing safety. The paper also provides knowledge relevant to future technologies (e.g. development of autonomous vehicles and smart roads). Rigour comes from the largest dataset ever studied in relation to the topic (over 21k drivers). Industry contact: Mohammad Mesgarpour, Head of Data Science and Operational Research, Microlise https://www.linkedin.com/in/mmesgarpour/?originalSubdomain=uk
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
