Enhancing Transportation Energy Demand Forecasting: A Comparative Analysis of Optimization Algorithms and Regression Models

Energy demand in the transportation sector has become a significant concern due to global economic growth, urbanization, and increased industrial production. This study focuses on the transportation energy demand (TED) problem. The primary dataset includes gross domestic product, population size, and vehicle-kilometer data. Nine different forecasting models were developed by optimizing the weight parameters of linear, exponential, and quadratic regression models using optimization algorithms such as the differential evolution algorithm (DEA), gravitational search algorithm, and crow search algorithm. These algorithms have not been used in TED forecasting before. This study aims to assess the effectiveness of forecasting models and improve their accuracy by addressing gaps in the literature. The models’ performance is evaluated using global error measurement techniques, and the results indicate that DEA outperforms other algorithms and is highly successful, as supported by the literature. The quadratic model of the DEA algorithm was the most successful algorithm with the highest R 2 value (0.987540) and the lowest mean absolute percent error value (5.2415%) compared to other methods. These findings are essential for decision-makers seeking to establish sustainable energy policies and provide a fundamental reference point for energy strategies, transportation planning, and policy-making.