Issue: 2026/Vol.36/No.4, Pages
MULTI-COMPONENT HYBRID DEEP LEARNING MODEL FOR RAILWAY PASSENGER DEMAND FORECASTING
Iqbal Kharisudin
, Merlinda Lavenia
This is not yet the definitive version of the paper. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article.
Cite as: I. Kharisudin, M. Lavenia. Multi-component hybrid deep learning model for railway passenger demand forecasting. Operations Research and Decisions 2026: 36(4).
Abstract
Railway transportation plays a critical role in supporting sustainable mobility in Indonesia, yet significant fluctuations in passenger demand often lead to congestion and operational challenges. This study presents a systematic evaluation of decomposition-based forecasting frameworks for railway passenger demand prediction by integrating Seasonal-Trend Decomposition using Loess (STL), Empirical Mode Decomposition (EMD) applied to residual components, and Fuzzy C-Means (FCM) clustering. Using the Argo Muria train service as a case study, multiple deep learning models, including LSTM, GRU, RNN, CNN, and BiLSTM, are trained on decomposed components, and their forecasts are combined linearly. Model performance is evaluated using a rolling-origin strategy across multiple stations. At the primary destination station, Semarang-Gambir, the best configuration achieves an MAE of 19.88, RMSE of 26.79, sMAPE of 8.97, and $R^2$ of 0.84. Consistent results across stations demonstrate the framework's robustness and generalization capability.
Keywords: signal decomposition, fuzzy clustering, time series forecasting, railway passenger demand, sustainable transportation
Received: 12 March 2026 Accepted: 16 August 2026
Published online: 16 August 2026