Understanding Factors Affecting Fuel Consumption of Vehicles Through Explainable AI: A Use Case With Explainable Boosting Machines
2021
A significant economic cost for many companies that operate with fleets of
vehicles is related to their fuel consumption. This consumption can be reduced
by acting over some aspects, such as the driving behaviour style of vehicle
drivers. Improving driving behaviour (and other features) can save fuel on a
fleet of vehicles without needing to change other aspects, such as the planned
routes or stops. This is important not only for mitigating economic costs
within a company, but also for reducing the emissions associated to fuel
consumption, mainly when the vehicles have petrol or diesel engines. In this
paper we show how Explainable Artificial Intelligence (XAI) can be useful for
quantifying the impact that different feature groups have on the fuel
consumption of a particular fleet. For that, we use Explainable Boosting
Machines (EBM) that are trained over different features (up to 70) in order to
first model the relationship between them and the fuel consumption, and then
explain it. With it, we compare the explanations provided by the EBM with
general references from the literature that estimate the potential impact that
those features may have on the fuel consumption, in order to validate this
approach. We work with several real-world industry datasets that represent
different types of fleets, from ones that have passenger cars to others that
include heavy-duty vehicles such as trucks.
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