Modeling, analysis and forecasting of the Jordan’s transportation sector energy consumption using artificial neural networks

This paper aims to employ Artificial Neural Networks (ANNs) to model, analyze, and forecast energy consumption and needs in the transportation sector of Jordan. The study investigates four key factors: the number of registered vehicles, income level, ownership level, and fuel prices. Data on energy consumption and the independent variables are collected from government and literature sources spanning the years 1985–2020. Various ANNs are carefully examined and optimized to ensure reliable solution convergence. The findings indicate that energy consumption in Jordan’s transportation sector is primarily influenced by the number of vehicles on the road and income levels. Moreover, the ANN-based energy consumption predictions demonstrate higher accuracy when compared to existing literature models. Consequently, the developed ANN model is utilized to forecast the energy requirements of Jordan’s transportation sector for the coming decade. Finally, this paper offers policy and legislation recommendations to assist decision-makers in ensuring a secure future for the country’s transportation sector.