Data-driven railway management: Forecasting monthly train passengers on the Surabaya-Jakarta route using XGBoost algorithm

Forecasting train passenger demand is essential for supporting strategic decision-making and optimizing resource allocation in the transportation industry. This study aimed to develop a predictive model for the number of passengers on the Surabaya-Jakarta train route using the Extreme Gradient Boosting (XGBoost) algorithm. Owing to the non-linear nature of the historical count time series data (January 2019 to December 2024) and the significant disruptive impact of the COVID-19 pandemic, traditional linear models such as ARIMA were considered less appropriate. To optimize the XGBoost model, we comparatively evaluated two distinct input approaches: significant Partial Autocorrelation Function (PACF) lag and the sliding window method. Hyperparameter tuning was conducted via grid search, and the models were rigorously evaluated using Time Series Cross-Validation to prevent information leakage. Furthermore, the study compared recursive and direct multi-step forecasting strategies to project passenger volumes for the next 12 months. The analysis revealed that the sliding window approach with a window size of 4 yielded the best performance on the testing data, achieving a Mean Absolute Percentage Error (MAPE) of 10.94% and significantly outperforming the PACF lag method, which was prone to overfitting. Additionally, recursive forecasting is more rational and effective at capturing complex seasonal patterns and short-term fluctuations than direct forecasting. The final 12-month projection for 2025 indicates clear seasonal fluctuations, with a low in March (10,319 passengers) and a peak in November (20,932 passengers), providing a data-driven foundation for the train company to proactively optimize capacity planning, operational scheduling, and human resource management in the future.