LOGI - Scientific Journal on Transport and Logistics | 2026
Authors: Khumla P.; Sarawan K.
DOI: 10.2478/logi-2026-0002
Journal: LOGI - Scientific Journal on Transport and Logistics
Year: 2026
Publisher: Sciendo
Document Type: Article
Open Access: All Open Access; Gold Open Access; Green Open Access
Cited by: 0
This study presents a novel forecasting framework that integrates Genetic Algorithms (GA) with Long Short-Term Memory (LSTM) networks to enhance the prediction accuracy of passenger volumes in Thailand’s public transportation systems. The unique contribution of this research lies in leveraging GA for automatic hyperparameter optimization of LSTM models, improving performance over conventional methods. The dataset comprises weekly aggregated passenger counts from road and rail modes between January 2020 and August 2023. The proposed GA-enhanced LSTM model (LSTM+GA) is evaluated using four metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Median Absolute Percentage Error (MdAPE). Results demonstrate that LSTM+GA outperforms baseline models including Autoregressive Integrated Moving Average (ARIMA), Facebook Prophet (FBProphet), and standard LSTM, achieving a MAPE of 4.04 for road and 5.86 for rail datasets. These findings suggest that the proposed model offers a practical and scalable tool for transport planners and public agencies seeking to optimize forecasting strategies and decision-making. © 2026 P. Khumla and K. Sarawan.
Forecasting; forecasting passenger numbers; genetic algorithms; long short-term memory; public transportation systems