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Towards real-time heavy goods vehicle driving behaviour classification in the United Kingdom

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Utkarsh Agrawal, Jimiama Mafeni Mase, Grazziela P. Figueredo, Christian Wagner, Mohammad Mesgarpour, Robert I. John

School/Research organisations


Determining the driving styles and the factors causing incidents in real time could assist stakeholders to promote actions and develop feedback systems to reduce risks, costs and to increase safety in roads. This paper presents a classification system for Heavy Goods Vehicles (HGVs) drivers, using a core set of driving pattern stereotypes which were uncovered from driving incidents across three years i.e. 2014, 2015 and 2016. To achieve that, the driving stereotypes are established by employing a 2-stage ensemble classification framework followed by a profile labelling algorithm to define the set of driving stereotypes. Very similar stereotypes are later merged to form the core driving stereotypes for UK HGV drivers. Upon establishing core driving stereotypes across these three years, a decision tree classifier learns the classification rules to identify the driving stereotypes for the HGV drivers in a new dataset. High accuracy is achieved, indicating that the core driving patterns uncovered in this work could potentially be employed to identify UK HGV driving patterns in real-time.


Original languageEnglish
Title of host publication2019 IEEE International Intelligent Transportation Systems Conference (ITSC)
ISBN (Electronic)9781538670248
Publication statusPublished - 27 Oct 2019
EventIEEE Intelligent Transportation Systems Conference (ITSC 2019) - Auckland, New Zealand
Duration: 27 Oct 201930 Oct 2019


ConferenceIEEE Intelligent Transportation Systems Conference (ITSC 2019)
Abbreviated titleITSC 2019
CountryNew Zealand
Internet address

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ID: 263044249