| Forecasting transportation demand is essential for the effective planning and operation of infrastructure and related services. Accurate predictions are crit-ical to ensuring the efficient performance of transport systems and meeting evolving user needs. This study analyzes the key factors influencing trans-portation 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 mod-eling and forecasting of transportation infrastructure demand. SSA, a non-parametric method for decomposing time series into lower-dimensional in-terpretable components, is employed to extract significant features such as trend, periodicities, and noise. This hybrid approach enhances model predic-tive accuracy while supporting efficient handling of large datasets. The methodology is applied to a real-world case study on the Egnatia Odos Mo-torway, focusing on the Iasmos–Komotini toll station over a four-year peri-od. The models are trained using the hold-out validation method and evalu-ated based on the coefficient of determination (R²) and Mean Squared Error (MSE). |
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