Forecasting transportation demand is essential for the effective planning and operation of infrastructure and related services. Accurate predictions are critical to ensuring the efficient performance of transport systems and meeting evolving user needs. This study analyzes the key factors influencing transportation demand and explores their implications for infrastructure planning and management. The primary objective is to integrate Singular Spectrum Analysis (SSA) with Artificial Neural Networks (ANNs) for time series modeling and forecasting of transportation infrastructure demand. SSA, a non-parametric method for decomposing time series into lower-dimensional interpretable components, is employed to extract significant features such as trend, periodicities, and noise. This hybrid approach enhances model predictive accuracy while supporting efficient handling of large datasets. The methodology is applied to a real-world case study on the Egnatia Odos Motorway, focusing on the Iasmos–Komotini toll station over a four-year period. The models are trained using the hold-out validation method and evaluated based on the coefficient of determination (R2) and Mean Squared Error (MSE).