Mental models of eco-driving: The measurement and activation of drivers’ knowledge and skills
Year: 2015
Type: dissertation
Abstract: Eco-driving has the potential to reduce fuel consumption and therefore emissions considerably. Previous research suggests that drivers already possess a certain level of eco-driving capability, which they do not practise in their everyday lives. The studies reported in this thesis are based on a mental models approach, which enabled an in-depth exploration of eco-driving knowledge and skills and broadened the understanding of the underlying cognitive mechanisms. \nThis thesis describes two driving simulator experiments aiming to measure, activate and ultimately interrupt eco-driving mental models in a variety of scenarios, relevant for safe and eco-driving. The studies used simple driving task instructions, text message interventions as well as a workload task. Changes in the participants’ behaviour and thoughts were analysed with a combination of quantitative and qualitative methods. \nThe results suggest that drivers have mental models of eco-driving on several levels, ranging from knowledge and strategies to tactics in specific situations to automated behaviour. However, in the first experiment they did not use them when they were instructed to ‘Drive normally’. In the second study text message primes and advice provided over two weeks were not able to replicate the effect of experimental instructions given directly before driving. Behavioural changes following these instructions were abandoned when performing a workload task, and not resumed afterwards. \nFuture research needs to consider alternative methods to prompt drivers to use their existing eco-driving knowledge and skills. Studies with a larger number of participants, and in real-world settings can then validate findings. It is suggested to expand the mental models approach into other fields such as sustainable transport in general. \n
Author: Sanna M. Pampel
Language: English
Subfield: Human Factors and Ergonomics
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2431707715