# Public Transport Demand Forecasting

Using one year of hourly boarding data from Budapest metro line M4 (216 usable days, 05:00–24:00) restricted to a single station and direction (Kálvin tér toward Kelenföld vasútállomás) and a 6-hour forecast window, the study fairly compared inherently interpretable NeuralProphet with a black-box XGBoost interpreted post hoc via SHAP, finding XGBoost more accurate (MAE 91.0753 vs 230.2803, RMSE 144.7094 vs 363.2115, R-squared 0.9600 vs 0.7896) and SHAP temporal-feature insights consistent with NeuralProphet seasonality components and transport expert knowledge.

Using one year of hourly boarding data from Budapest metro line M4 (216 usable days, 05:00–24:00) restricted to a single station and direction (Kálvin tér toward Kelenföld vasútállomás) and a 6-hour forecast window, the study fairly compared inherently interpretable NeuralProphet with a black-box XGBoost interpreted post hoc via SHAP, finding XGBoost more accurate (MAE 91.0753 vs 230.2803, RMSE 144.7094 vs 363.2115, R-squared 0.9600 vs 0.7896) and SHAP temporal-feature insights consistent with NeuralProphet seasonality components and transport expert knowledge.