A supervised learning approach to estimate the drivers impact on fuel consumption : A heavy-duty vehicle case study

The aim of this Master thesis is to provide a statistical analysis of the factors inuencing the fuel consumption, with a focus on the separation of the drivers' performance. The study is focused on the long haulage trucks, which correspond to the application where the fuel consumption becomes of primary interest from the economical point of view. Further developments of the work leads to a graphical representation of the outcomes on a map, highlighting in particular the segments of the road network having the highest variation of the driver-inuenced fuel consumption. The analysis dataset created is the combination of data coming from di erent sources and additional features computed based on them. The datasources are providing respectively the vehicles' operating data and congurations, the road network's characteristics and the weather information. The results obtained prove that it is possible to isolate the driver factor from the overall fuel consumption. This can be achieved by training a model composed by variables statistically chosen through a regression procedure. Further in the analysis the di erent driver factors are used in order to determine the fuel saving potential of the road stretches where the factors are computed. The results are gathered in multiple stages, based on the dimension of the dataset considered and the method used. Two methods are used to train the model: the least squares regression and the ridge regression. First the whole Swedish road network composed by primary roads is analyzed with least squares. 1195 road stretches belonging to this network present a dened and di erent than zero fuel saving potential varying between 0.003 and 83.71 l/100km. Then, a smaller portion of the same road network is analyzed after being provided with road slope information. The fuel saving potential estimated using ridge regression present values between 0.002 and 24.39 l/100km. From the geographical point of view little can be deduced from the analysis of the complete network. The E4 provided with slope data, on the contrary, allows a better insight, especially using ridge