Deep learning approach for short-term entry passenger flow forecasting in urban rail transit stations

In short-term passenger flow forecasting for urban rail transit, Automatic Fare Collection (AFC) data serves as a crucial carrier reflecting the characteristics of passenger flow variations. Existing short-term passenger flow prediction methods primarily rely on historical time-series data from individual stations, yet similar passenger flow trends may exist across different stations. To comprehensively capture the passenger flow characteristics of various station types and enhance the interpretability of predictions, this paper proposes a CEEMDAN-TCAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Temporal Convolutional Attention Network) method based on two-step clustering. First, a two-step fuzzy k -means (F k M) approach is employed to perform spatial feature extraction based on land use patterns and passenger flow characteristics around metro stations, thereby providing a more generalized dataset for station-level predictions. Subsequently, CEEMDAN is applied to deeply extract multi-scale temporal features of passenger flow, improving the prediction accuracy of station entry passenger volumes by addressing the automatic adjustment of non-stationary time-series data. Next, an encoder-decoder framework is adopted for station-level passenger flow prediction, and the model's rationality is validated using attention score heatmaps at different step lengths and partial autocorrelation coefficients. To verify the effectiveness of the proposed method and model, extensive experiments are conducted using historical passenger flow data from Suzhou Metro stations. The results demonstrate that the proposed model achieves 1.7 %–36.9 % higher prediction accuracy compared to other classical models while reducing average training time by over 55.8 %. • A two-step clustering approach is introduced for metro station classification. • Sparse attention mechanisms are employed to enhance prediction interpretability. • A hybrid model is adopted for forecasting station entry passenger flow. • The proposed model achieves 1.7 %–36.9 % lower MAPE compared to existing methods.