As urban populations grow and traffic congestion increases, public transportation systems offer a sustainable alternative by reducing dependence on private vehicles and lowering carbon emissions. Moreover, understanding station demand is particularly challenging in fare-free systems such as Luxembourg's, where traditional ticketing data is unavailable. This study presents a novel framework for forecasting bus station demand using Google Popular Times (GPT) data through a two-stage deep learning methodology. We develop a predictive Sequence-to-Sequence (Seq2Seq) model that forecasts station occupancy levels over a 24-hour horizon based on the previous 72 hours of data.

The results shows that station-specific Seq2Seq models outperformed a single city-wide model (RMSE=11.50, MAE=8.94, R²=0.67), demonstrating benefits of capturing individual station characteristics. Results demonstrated that the models effectively captured temporal dependencies, with RMSE, MAE, and R² metrics confirming robust performance across stations.