Eco-Driving Level Evaluation Model for Electric Buses Entering and Leaving Stops

This paper evaluates the ecological level of driving behavior of electric buses when entering and leaving stops. A dataset of entering and leaving stops is first created based on the natural driving data of electric buses. The representative parameters of driving behaviors for entering and leaving stops are then selected through correlation analysis and multiple stepwise linear regression analysis. Afterwards, the threshold value for defining the eco-driving behavior is determined by analyzing the energy consumption characteristics of entering and leaving stops. Finally, the Random Forest (RF), Gradient-Boosted Decision Trees (GBDT), and Light Gradient Boosting Machine (LightGBM) algorithms are applied to develop the evaluation models of eco-driving level for entering and leaving stops. The obtained results show that the accuracies of the LightGBM model for the evaluation of the eco-driving level in entering and leaving stops are 89% and 86.7%, respectively. These values are better than those of the RF and GBDT algorithms, and thus they demonstrate that the LightGBM model can more accurately evaluate the eco-driving in entering and leaving stops.