Before revision:  We use Explainable Boosting Machines (EBM), trained over different features in order to both model and explain the relationship between them and fuel consumption, and then assess quality of the explanations using prior domain knowledge . We work with real-world industry datasets that represent different types of vehicles, from passenger cars to heavy-duty trucks .
After revision:  We use Explainable Boosting Machines (EBM), trained over different features in order to both model and explain the relationship between them and fuel consumption, quantifying the individual contribution of each one of them. The explanations provided by the model are compared with domain knowledge in order to see if they are aligned. The results show that the 70\% of the categories associated to the fuel-factors are similar to the previous literature. With the EBM algorithm, we estimate that optimizing driving behaviour decreases fuel consumption between 12\% and 15\% in a large fleet (more than 1000 vehicles) .