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dc.contributor.authorAgrawal, Utkarsh
dc.contributor.authorMase, Jimiama Mafeni
dc.contributor.authorFigueredo, Grazziela P.
dc.contributor.authorWagner, Christian
dc.contributor.authorMesgarpour, Mohammad
dc.contributor.authorJohn, Robert I.
dc.identifier.citationAgrawal , U , Mase , J M , Figueredo , G P , Wagner , C , Mesgarpour , M & John , R I 2019 , Towards real-time heavy goods vehicle driving behaviour classification in the United Kingdom . in 2019 IEEE International Intelligent Transportation Systems Conference (ITSC) . IEEE , pp. 2330-2336 , IEEE Intelligent Transportation Systems Conference (ITSC 2019) , Auckland , New Zealand , 27/10/19 .
dc.identifier.otherPURE: 263044249
dc.identifier.otherPURE UUID: 5adedeef-8e05-4aae-acf6-1b4b4da873bb
dc.identifier.otherScopus: 85076823832
dc.description.abstractDetermining 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.
dc.relation.ispartof2019 IEEE International Intelligent Transportation Systems Conference (ITSC)en
dc.rightsCopyright © 2019 IEEE. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at
dc.subjectHE Transportation and Communicationsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.titleTowards real-time heavy goods vehicle driving behaviour classification in the United Kingdomen
dc.typeConference itemen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen

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