Deep learning-based public transport demand forecasting

As cities grow more crowded, efficient public transport systems become more essential. The digitalization of public transport generates large amounts of data. This data creates new opportunities for data-driven optimization. One key application of such data is demand forecasting, which is essential for effective planning and resource allocation. However, traditional methods struggle to handle the complexity of urban transit data. As a result, recent research has focused on deep learning models which achieve higher accuracy by capturing complex temporal and spatial patterns. This thesis introduces Mini-PatchTST, a lightweight variant of PatchTST, designed for metro passenger demand forecasting with reduced complexity. To support this work, a novel station-level hourly metro passenger flow dataset was constructed using open data from the Istanbul Metropolitan Municipality. The proposed approach is bench- marked against traditional statistical methods, machine learning models such as sta- tistical methods, machine learning models (LightGBM, XGBoost), and deep learning baselines (LSTM, PatchTST). Experimental results on the Istanbul Metro dataset show that Mini-PatchTST outper- forms gradient-boosting models with a 5–13% RMSE reduction while maintaining com- parable MAE. It achieves a MAPE of 23.42%, substantially improving upon PatchTST (33.89%) though remaining slightly higher than tree-based models (15–16%). Compared to the original PatchTST, Mini-PatchTST reduces RMSE by 4%, MAE by 10%, and MAPE by nearly 31%. Although LSTM attains the lowest RMSE and MAE, its higher MAPE and significantly longer training time highlight the superior efficiency–accuracy trade-off of Mini-PatchTST. To assess generalization capability, the model is further evaluated on the open-source Hangzhou Metro dataset, where it yields substantial im- provements over models used in prior studies. The findings indicate that Mini-PatchTST provides a robust, and computationally efficient solution for short-term public transport demand forecasting.