Identifying behavioural change among drivers using Long Short-Term Memory recurrent neural networks. (February 2018)

• Record Type: Journal Article
• Authors: Wijnands, Jasper S.; Thompson, Jason; Aschwanden, Gideon D.P.A.; Stevenson, Mark
• Is Part Of: Transportation research. Volume 53(2018)
• Page Start: 34
• Page End: 49
• Publication Date: 2018-02
• Subjects: Driver -- Behavior -- Neural network -- Long short-term memory -- Feedback -- Transportation; Automobile drivers -- Psychology -- Periodicals; Automobile driving -- Psychological aspects -- Periodicals; Transportation -- Psychological aspects -- Periodicals; 629.283019
• DOI: 10.1016/j.trf.2017.12.006
• Languages: English
• ISSNs: 1369-8478

Abstract

Highlights: Human errors cause the majority of road crashes, leading to deaths and injuries. The provision of timely feedback has a positive impact on driving behaviour. A new methodology based on Long Short-Term Memory neural networks is presented. The method identifies behavioural change using in-vehicle telematics technology. Feedback may prevent internalisation of new, risky habits and reduce crash risk. 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 personalised driver profile, with neural networks to identify changes in driving behaviour. Using Long Short-Term Memory (LSTM) recurrent neural networks, individual drivers are identified based on their pattern of acceleration, deceleration and exceeding the speed limit. After model calibration, new, real-time data of the driver is supplied to the LSTM and, by monitoring prediction performance, one can assess whether a (positive or negative) change in driving behaviour is occurring over time. The paper highlights that the approach is robust to different neural network structures, data selections, calibration settings, and methodologies to select benchmarks for safe and unsafe driving. Presented case studies show additional model applications for investigating changes in driving behaviour among individuals following or during specific events (e.g., receipt of insurance renewal letters) and time periods (e.g., driving during holiday periods). The application of the presented methodology shows potential to form the basis of timely provision of direct feedback to drivers by telematics-based insurers. Such feedback may prevent internalisation of new, risky driving habits contributing to crash risk, potentially reducing deaths and injuries among young drivers as a result.