A data analysis framework to rank HGV drivers
We report on the details of the methodology applied to support shortlisting the nominees for the Microlise Driver of the Year awards. The aim was to recognise the United Kingdom’s most talented heavy goods vehicle (HGV) drivers, with the list of top 46 drivers across 16 different companies determine...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
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2015
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| Online Access: | https://eprints.nottingham.ac.uk/49963/ |
| _version_ | 1848798121351446528 |
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| author | Figueredo, Grazziela P. Quinlan, P. Mesgarpour, Mohammad Garibaldi, Jonathan M. John, Robert |
| author_facet | Figueredo, Grazziela P. Quinlan, P. Mesgarpour, Mohammad Garibaldi, Jonathan M. John, Robert |
| author_sort | Figueredo, Grazziela P. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | We report on the details of the methodology applied to support shortlisting the nominees for the Microlise Driver of the Year awards. The aim was to recognise the United Kingdom’s most talented heavy goods vehicle (HGV) drivers, with the list of top 46 drivers across 16 different companies determined through the analysis of telematics data. Initial data for the awards was gathered from over 90,000 drivers engaging with Microlise’s telematics solutions. The data was analysed anonymously in order to identify the best criteria to establish top performing drivers. The initial selection was made based on a minimum number of miles driven across each of the four quarters in 2014. Outlier removal and a consensus clustering framework were subsequently employed to the dataset to identify subgroups of drivers. Three categories of drivers were identified: short, medium and long distance drivers. Each qualifying professional belonging to one of the three categories was then assessed using a range of criteria compared to other drivers from the same category. To determine the final winners, questionnaires for further evidence and indicators that might contribute to a driver being named as a winner was sent down to employers and their responses were evaluated. |
| first_indexed | 2025-11-14T20:14:44Z |
| format | Conference or Workshop Item |
| id | nottingham-49963 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:14:44Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-499632020-05-04T17:17:07Z https://eprints.nottingham.ac.uk/49963/ A data analysis framework to rank HGV drivers Figueredo, Grazziela P. Quinlan, P. Mesgarpour, Mohammad Garibaldi, Jonathan M. John, Robert We report on the details of the methodology applied to support shortlisting the nominees for the Microlise Driver of the Year awards. The aim was to recognise the United Kingdom’s most talented heavy goods vehicle (HGV) drivers, with the list of top 46 drivers across 16 different companies determined through the analysis of telematics data. Initial data for the awards was gathered from over 90,000 drivers engaging with Microlise’s telematics solutions. The data was analysed anonymously in order to identify the best criteria to establish top performing drivers. The initial selection was made based on a minimum number of miles driven across each of the four quarters in 2014. Outlier removal and a consensus clustering framework were subsequently employed to the dataset to identify subgroups of drivers. Three categories of drivers were identified: short, medium and long distance drivers. Each qualifying professional belonging to one of the three categories was then assessed using a range of criteria compared to other drivers from the same category. To determine the final winners, questionnaires for further evidence and indicators that might contribute to a driver being named as a winner was sent down to employers and their responses were evaluated. 2015-09-15 Conference or Workshop Item PeerReviewed Figueredo, Grazziela P., Quinlan, P., Mesgarpour, Mohammad, Garibaldi, Jonathan M. and John, Robert (2015) A data analysis framework to rank HGV drivers. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems (ITSC 2015), 15-18 Sept 2015, Las Palmas, Spain. http://ieeexplore.ieee.org/document/7313416/ |
| spellingShingle | Figueredo, Grazziela P. Quinlan, P. Mesgarpour, Mohammad Garibaldi, Jonathan M. John, Robert A data analysis framework to rank HGV drivers |
| title | A data analysis framework to rank HGV drivers |
| title_full | A data analysis framework to rank HGV drivers |
| title_fullStr | A data analysis framework to rank HGV drivers |
| title_full_unstemmed | A data analysis framework to rank HGV drivers |
| title_short | A data analysis framework to rank HGV drivers |
| title_sort | data analysis framework to rank hgv drivers |
| url | https://eprints.nottingham.ac.uk/49963/ https://eprints.nottingham.ac.uk/49963/ |