Optimization Method to Improve Ecodriving Acceptance and Effectiveness Based on Driver Type Classification

Abstract

Not all drivers are motivated by the same values (i.e., they may have proself [Editor’s note: The authors use the term “proself” to describe individuals who focus on their own well-being.] or prosocial [Editor’s note: The authors use the term “prosocial” to describe individuals who focus more on the well-being of others than on their own well-being.] values), and different drivers might prefer to learn in different ways (i.e., through learning or performance). To improve drivers’ acceptance of ecodriving and to enhance the effectiveness of ecodriving training, an ecodriving behavior optimization method that considers the characteristics of driver types and the effectiveness of approaches to ecodriving training was developed in this study. On the basis of the relationship between drivers’ values and goal orientations and their driving behaviors, data on in-trip driving behavior were collected, and then the drivers were classified into four types: those with proself and learning orientations, those with proself and performance orientations, those with prosocial and learning orientations, and those with prosocial and performance orientations. The effectiveness of three common ecodriving training methods (i.e., education, coaching, and feedback through smartphone applications) was then tested and compared to determine those that met the fuel consumption characteristics of the driver types. In addition, feedback corresponding to driver types was designed (e.g., how much money they saved from ecodriving and their ecodriving rankings compared with the rankings of their peers). Finally, the appropriate ecodriving training method and suitable feedback were identified for each driver type. A validation test showed that this ecodriving behavior optimization method was more effective in reducing fuel consumption than the generic ecodriving training method (by which the reductions in fuel consumption were 9.60% and 4.62%, respectively). The study results provide guidance for ecodriving applications, contributing to the improvement of vehicle fuel use efficiency.