Forecasting the Passenger Car Demand Split from Public Perceptions of Electric, Hybrid, and Hydrogen-Fueled Cars in Greece

Abstract

Efforts to reduce greenhouse gas emissions from the land transport sector revolve around replacing the Internal Combustion Engine with alternative power units. Indeed, governments within the European Union and beyond move to ban the sale of new internal combustion engine vehicles in the near future. A number of technologies are proposed as alternatives, such as electric motors powered by batteries or hydrogen fuel cells, and hybrid power units. These new technologies rely on new infrastructure (charging stations, electrical grid upgrades, hydrogen production, storage and fueling facilities), which will need to be put in place to meet the needs of a transforming vehicle fleet. As such, forecasting the demand for the different technologies will be crucial in planning investments. We use machine learning techniques, specifically a Multilayer Perceptron and an Adaptive Neural Fuzzy Inference System, to forecast the demand split from public perceptions as captured through an online survey.