FORECAST OF AN EFFICIENCY OF ALTERNATIVE FUEL PARK GROWTH BASED ON THE LOGISTIC REGRESSION MODEL

Abstract

In this study, a model is built that allows predicting the effectiveness of government support measures for the development of vehicles using alternative fuels. The model is based on machine learning approach. A software module for its implementation in the Python language is proposed. The effectiveness of state support measures is understood as their impact on the growth of the fleet using alternative fuels. The model has been trained and tested on data for European countries on the number of electric vehicles (data from the European Observatory for the Use of Alternative Fuels), however, the developed software module allows the user to independently train the model and make forecasts for any other types of road vehicles using alternative fuels (biofuel, liquefied natural gas).