Eco-driving incentives in the North American market

Abstract

Finding the correct incentives that will nudge drivers to improve their fuel efficiency is the key for achieving long-term change in their driving behavior. In this paper we discuss a participant study which is designed to provide insights on driver attitudes, behaviors and habits. This study focuses on residents in the San Francisco Bay area, and analyzes the eco-topic from multiple perspectives such as people's everyday routine, purchasing decisions, and driving habits.The results show how income, age and gender of participants relate to their eco-friendliness. The most preferred types of information as well as visualizations for displaying fuel consumption and efficiency information in the car are discussed. Lastly, appropriate reward types for eco-driving are ranked and show what participants prefer best. The methodology and results of this research contribute to the design of automotive interfaces that may help people change their behavior and improve their fuel efficiency.

The impact of numerical vs. symbolic eco-driving feedback on fuel consumption – A randomized control field trial

“…1c), received lower user acceptance ratings. Similar results about subjective EDFIS design evaluations were found in other surveys (Loumidi et al, 2011;Tulusan, 2013) and in focus group studies (Jenness et al, 2009;Vaezipour et al, 2017). Beyond the transportation research community, scholars from other disciplines, such as environmental psychology or human computer interaction (HCI), have studied the design of similar feedback systems, (Froehlich et al, 2012(Froehlich et al, , 2010Lockton et al, 2017).…”

“…In designing the G-radar, we had to strike a balance between minimizing the amount of information that might already have an impact on eco-driving and making the display sufficiently interesting for the control group so that the drivers would use it as much as possible to avoid biases resulting from usage attrition (Hausman and Wise, 1979). On the one hand, exposing also the control group to a non-static screen with driving metrics mitigates differences in potentially confounding Hawthorne effects (also see Section 3.2): The mere presence of a screen with driving metrics may remind the study participants that their behaviour is being monitored, which may alter their behaviour (Macefield, 2007;Tiefenbeck et al, 2018). By exposing all groups alike to a screen with driving metrics, we eliminate that potential source of bias.…”