Eco-driving strategies in lane-change behaviors use: How do drivers reduce fuel consumption?

To improve the energy efficiency and reduce emissions of motor vehicles, this study tests and compares five machine learning algorithms in conjunction with three sets of feature indicators to establish an assessment model for the ecological nature of lane-changing behavior. The model combining the Extreme Gradient Boosting (XGBoost) algorithm and the Trend Feature Symbolic Aggregate Approximation (TFSAX) feature metrics set performs well. The effectiveness of the TFSAX feature metrics set in capturing factors influencing vehicle fuel consumption and driving behavior sequence features was also verified. Furthermore, it was concluded that the specific value of pedal pressing depth is not the primary factor contributing to differences in fuel consumption levels; rather, the magnitude of its trend largely determines fuel consumption levels. Therefore, the model we have developed has important applications in assessing the ecological aspects of lane-changing behavior on urban roads.