Before revision:  Improving these factors can reduce the fuel usage of a vehicle without changing other aspects, such as planned routes or stops. This mitigates economic costs while reducing emissions associated to fuel consumption. In this paper we show how Explainable Artificial Intelligence (XAI) is useful for quantifying the impact that fuel factors have on the consumption of a vehicle fleet. 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 .
After revision:  In this study, we developed an Explainable Boosting Machine (EBM) model to predict fuel consumption of different types of industrial vehicles, using real-world data collected from 2020 to 2021. This Machine Learning model also explains the relationship between them and fuel consumption, and then assess quality of the explanations using prior domain knowledge .