Prediction of transportation energy demand: Multivariate Adaptive Regression Splines

Abstract

Energy usage in the transportation sector has been increasing in Turkey. Good management of energy is important as well as a reliable prediction of the energy demand in the transportation sector. The main objective of this research is to predict transport energy demand using Multivariate Adaptive Regression Splines (MARS) as a nonparametric regression technique. Transport energy demand was modeled for the period 1975–2019 based on a mix of factors including the gross domestic product (GDP), population, vehicle-km, ton-km, passenger-km and oil price. Five models were established and compared with real data collected from the Ministry of Energy and Natural Resources (MENR). Five MARS models including pairs of predictors, i.e. oil price-GDP, oil price-population, oil price-ton, oil price-vehicle and oil price-passenger, were evaluated comparatively in the prediction of energy demand. Among the candidate models, the third MARS model, which had the lowest RMSE, SD ratio, AICc values and the highest R2, Adjusted R2 and especially GR2 value, was selected as the best predictive model. In conclusion, it could be suggested that the third MARS model produced the highest predictive performance in the prediction of energy demand by two predictors, ton and oil price.