A data analysis framework to rank HGV drivers

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.

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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.

Item Type: Conference or Workshop Item (Paper)
RIS ID: https://nottingham-repository.worktribe.com/output/761228
Additional Information: Included in 2015 IEEE 18th International Conference on Intelligent Transportation Systems : ITSC 2015. ISBN 9781467365956
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User: Patrocinio Figueredo, Grazziela
Date Deposited: 26 Feb 2018 10:56
Last Modified: 04 May 2020 17:17
URI: https://eprints.nottingham.ac.uk/id/eprint/49963

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