A machine learning approach for the estimation of fuel consumption related to road pavement rolling resistance for large fleets of trucks

The study shows that neural networks are suitable to analyse the large quantities of data, coming from fleet and road asset management databases, effectively and that the developed NN model is able to estimate the impact of rolling resistance-related parameters (pavement roughness and macrotexture) on truck fuel consumption.