TY - THES

T1 - Using telematics digital traces to predict individual differences in ecological driving

AU - Marquez, Holly

PY - 2023/9

Y1 - 2023/9

N2 - Engineering innovations in transport are insufficient alone to combat its effects on the climate crisis. ‘Driving style’ – the way a driver prefers to or habitually drives their vehicle – significantly impacts fuel consumption and exhaust emissions. However, changes from an ‘aggressive’ to a more refined style – ‘eco-driving’ – offers overlooked opportunities for emissions savings. In this thesis, I explore how individual differences including personality, wellbeing and aspects of demography are related to objective eco-driving behaviours in a sample of monitored drivers. By adopting an interdisciplinary approach, this thesis incorporates methods from psychology and computer science to consider both theoretical and methodological implications. Substantially, findings across the research point to an emerging and central role of emotion dysfunction as a key influence in drivers’ inefficient operational driving behaviours. Moreover, a clear intention – behaviour gap is identified between drivers’ self-report intentions to eco-drive and their objective eco-driving behaviours. Recommendations illustrate how these insights can be translated into digital behaviour change interventions (DBCI) to encourage sustained changes in drivers’ ecological driving efficiency.

AB - Engineering innovations in transport are insufficient alone to combat its effects on the climate crisis. ‘Driving style’ – the way a driver prefers to or habitually drives their vehicle – significantly impacts fuel consumption and exhaust emissions. However, changes from an ‘aggressive’ to a more refined style – ‘eco-driving’ – offers overlooked opportunities for emissions savings. In this thesis, I explore how individual differences including personality, wellbeing and aspects of demography are related to objective eco-driving behaviours in a sample of monitored drivers. By adopting an interdisciplinary approach, this thesis incorporates methods from psychology and computer science to consider both theoretical and methodological implications. Substantially, findings across the research point to an emerging and central role of emotion dysfunction as a key influence in drivers’ inefficient operational driving behaviours. Moreover, a clear intention – behaviour gap is identified between drivers’ self-report intentions to eco-drive and their objective eco-driving behaviours. Recommendations illustrate how these insights can be translated into digital behaviour change interventions (DBCI) to encourage sustained changes in drivers’ ecological driving efficiency.

KW - Eco-Driving

KW - Driving behaviour

KW - Environmental Driving

KW - Individual Differences

KW - Eco-efficiency

KW - Driving style

KW - Behaviour change

KW - Behavioural analytics

KW - Telematics

KW - pro-environmental behavior

KW - Eco-friendly

M3 - Master's Thesis

PB - Lancaster University

ER -