A significant economic cost for many companies that operate with fleets of diesel and petrol vehicles is related to fuel consumption. Consumption can be reduced by acting over some factors, like driving behaviour style. 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 . We work with real-world industry datasets that represent different types of vehicles, from passenger cars to heavy-duty trucks .
Fuel optimization of diesel and petrol vehicles within industrial fleets is critical for mitigating costs and reducing emissions. This objective is achievable by acting on fuel-related factors, such as the driving behaviour style. 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 the input factors 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) .
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A significant economic cost for many companies that operate with fleets
Fuel optimization
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is related to fuel consumption. Consumption can be reduced by acting over some factors, like
within industrial fleets is critical for mitigating costs and reducing emissions. This objective is achievable by acting on fuel-related factors, such as the
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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
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
R
them
the input factors
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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
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)