Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom

Figueredo, Grazziela P.; Agrawal, Utkarsh; Mase, Jimiama; Mesgarpour, Mohammad; Wagner, Christian; Soria, Daniele; Garibaldi, Jonathan M.; Siebers, Peer-Olaf; John, Robert

Abstract

Although driving behaviour has been largely studied amongst private motor vehicles drivers, the literature addressing heavy goods vehicle (HGV) drivers is scarce. Identifying the existing groups of driving stereotypes and their proportions enables researchers, companies and policy makers to establish group-specific strategies to improve safety and economy. In addition, insights into driving styles can assist predicting drivers' reactions and therefore enable the modelling of interactions between vehicles and the possible obstacles encountered on a journey. Consequently, there are also contributions to the research and development of autonomous vehicles and smart roads. In this study our interest lies in investigating driving behaviour within the HGV community in the United Kingdom (UK). We conduct the analysis of a telematics dataset containing incident information on 21,193 HGV drivers across the UK. We are interested in answering two research questions: (i) What groups of behaviour are we able to uncover? (ii) How do these groups complement current findings in the literature? To answer these questions we apply a two-stage data analysis methodology involving consensus clustering and ensemble classification to the dataset. Through the analysis, eight patterns of behaviour are uncovered. It is also observed that although our findings have similarities to those from previous work on driving behaviour, further knowledge is obtained, such as extra patterns and driving traits arising from vehicle and road characteristics.

Citation

Figueredo, G. P., Agrawal, U., Mase, J., Mesgarpour, M., Wagner, C., Soria, D., Garibaldi, J. M., Siebers, P.-O., & John, R. (2019). Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom. IEEE Transactions on Intelligent Transportation Systems, 20(9), 3324-3336. https://doi.org/10.1109/TITS.2018.2875343

• Journal Article Type: Article
• Acceptance Date: Oct 4, 2018
• Online Publication Date: Nov 20, 2018
• Publication Date: 2019-09
• Deposit Date: Oct 8, 2018
• Publicly Available Date: Oct 9, 2018
• Journal: IEEE Transactions on Intelligent Transportation Systems
• Print ISSN: 1524-9050
• Electronic ISSN: 1558-0016
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 20
• Issue: 9
• Pages: 3324-3336
• DOI: https://doi.org/10.1109/TITS.2018.2875343
• Keywords: Driver Profiling, Driving Pattern, Driving Habit, Driver Behaviour, Clustering Analysis, Ensemble Clustering, Ensemble Classification, Big Data Analysis
• Public URL: https://nottingham-repository.worktribe.com/output/1151928
• Publisher URL: https://ieeexplore.ieee.org/document/8541101
• Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
• Contract Date: Oct 8, 2018