Journal article

Identifying behavioural change among drivers using Long Short-Term Memory recurrent neural networks

J Wijnands, J Thompson, G Aschwanden, M Stevenson

Transportation Research Part F: Traffic Psychology and Behaviour | Elsevier | Published : 2018

Abstract

Globally, motor vehicle crashes account for over 1.2 million fatalities per year and are the leading cause of death for people aged 15–29 years. The majority of road crashes are caused by human error, with risk heightened among young and novice drivers learning to negotiate the complexities of the road environment. Direct feedback has been shown to have a positive impact on driving behaviour. Methods that could detect behavioural changes and therefore, positively reinforce safer driving during the early stages of driver licensing could have considerable road safety benefit. A new methodology is presented combining in-vehicle telematics technology, providing measurements forming a personalise..

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Grants

Awarded by Australian Research Council


Awarded by Victorian Life Sciences Computation Initiative (VLSCI)


Funding Acknowledgements

This work was supported by the Australian Research Council [Grant No. LP150100680]; Insurance Box Pty Ltd.; and the Transport Accident Commission. In addition, this research was supported by a Victorian Life Sciences Computation Initiative (VLSCI) [Grant No. UOM0016] on its Peak Computing Facility at the University of Melbourne, an initiative of the Victorian Government, Australia. M.S. was supported by a National Health and Medical Research Council (Australia) Fellowship. The in-vehicle telematics database was kindly provided by Frank Peppard of Insurance Box Pty Ltd., Australia for road safety research purposes. Finally, the authors would like to acknowledge the valuable feedback of the anonymous reviewers, which helped us to improve the quality of the original manuscript.