Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom
- Submitting institution
-
The University of Westminster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- q97qy
- 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
- Driving behaviours have been largely studied in literature, but specific research on heavy good vehicles (HGV) drivers is scarce. This work is significant, because it shows that a multi-stage framework - formed by clustering and classification steps - to elucidate groups within data can be applied to diverse domains like HGV incidents data, to identify new patterns in HGV drivers’ behaviours and inform companies, stakeholders, and even policy makers about future strategies to be adopted. This work generated impact, as the award of HGV ‘Driver of the Year’ was one of the consequences of this particular study.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
