Old habits die hard? The fragility of eco-driving mental models and why green driving behaviour is difficult to sustain

Pampel, Sanna M.; Jamson, Samantha L.; Hibberd, Daryl; Barnard, Yvonne

Abstract

Tangible incentives, training and feedback systems have been shown to reduce drivers’ fuel consumption in several studies. However, the effects of such tools are often short-lived or dependent on continuous cues. Several studies found that many drivers already possess eco-driving mental models, and are able to activate them, for instance when an experimenter asks them to “drive fuel-efficiently”. However, it is unclear how sustainable mental models are. The aim of the current study was to investigate the resilience of drivers’ eco-driving mental models following engagement with a workload task, implemented as a simplified version of the Twenty Questions Task (TQT). Would drivers revert to ‘everyday’ driving behaviours following exposure to heightened workload? A driving simulator experiment was conducted whereby 15 participants first performed a baseline drive, and then in a second session were prompted to drive fuel-efficiently. In each drive, the participants drove with and without completing the TQT. The results of two-way ANOVAs and Wilcoxon signed-rank tests support that they drive more slowly and keep a more stable speed when asked to eco-drive. However, it appears that drivers fell back into ‘everyday’ habits over time, and after the workload task, but these effects cannot be clearly isolated from each other. Driving and the workload task possibly invoked unrelated thoughts, causing eco-driving mental models to be deactivated. Future research is needed to explore ways to activate existing knowledge and skills and to use reminders at regular intervals, so new driver behaviours can be proceduralised and automatised and thus changed sustainably.

Citation

Pampel, S. M., Jamson, S. L., Hibberd, D., & Barnard, Y. (2018). Old habits die hard? The fragility of eco-driving mental models and why green driving behaviour is difficult to sustain. Transportation Research Part F: Traffic Psychology and Behaviour, 57, 139-150. https://doi.org/10.1016/j.trf.2018.01.005

• Journal Article Type: Article
• Acceptance Date: Jan 11, 2018
• Online Publication Date: Feb 4, 2018
• Publication Date: Aug 1, 2018
• Deposit Date: Feb 6, 2018
• Publicly Available Date: Feb 5, 2019
• Journal: Transportation Research Part F: Traffic Psychology and Behaviour
• Print ISSN: 1369-8478
• Electronic ISSN: 1873-5517
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 57
• Pages: 139-150
• DOI: https://doi.org/10.1016/j.trf.2018.01.005
• Keywords: Mental models; Driving simulator; Eco driving; Workload; Driver behaviour; Automatisation
• Public URL: https://nottingham-repository.worktribe.com/output/909615
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1369847817300414
• Contract Date: Feb 6, 2018