Journal of Advanced Transportation / 2024 / Article / Tab 3 / Research Article
Attention Mechanism with Spatial-Temporal Joint Deep Learning Model for the Forecasting of Short-Term Passenger Flow Distribution at the Railway Station Table 3 Comparison of prediction performances obtained using different TGs in different baseline models.
Time granularity 10 s 20 s 30 s 60 s Indicators RMSE MAE MAPE (%) RMSE MAE MAPE (%) RMSE MAE MAPE RMSE MAE MAPE (%) ARIMA 16.282 10.281 55.1 43.069 23.602 58.7 63.227 35.636 63.2% 120.975 63.829 88.1 BPNN 3.973 2.975 14.1 8.843 5.877 18.5 14.824 8.627 23.9% 44.443 28.725 26.6 CNN 5.961 3.239 18.4 10.604 8.685 22.3 31.238 14.611 27.4 47.961 25.614 28.6 RNN 5.626 5.879 13.8 9.365 6.174 18.3 30.962 12.962 23.9% 46.716 22.606 23.6 LSTM 5.203 5.808 13.7 8.722 5.114 17.5 28.806 10.589 23.9% 31.320 17.701 22.4 ST-Bi-LSTM (no graph) 2.913 2.214 12.7 7.047 4.961 17.2 14.878 8.534 23.3% 29.499 17.782 21.7 ST-Bi-LSTM (1A) 2.927 2.023 12.9 6.744 4.297 16.3 13.696 8.759 20.9% 34.015 22.683 31.4 ST-Bi-LSTM (no A) 2.849 2.094 12.6 6.614 4.291 15.9 13.247 8.191 20.1% 25.461 14.874 19.4 ST-Bi-LSTM (no T) 2.931 2.145 13.1 6.761 4.306 16.2 13.600 8.35 20.8% 28.103 16.912 20.7 ST-Bi-LSTM (no T&A) 2.986 2.966 13.5 6.651 4.395 16.5 13.855 8.527 20.2% 30.216 17.411 22.1 Adam ST-Bi-LSTM 2.889 2.018 13.0 6.640 4.309 16.2 13.377 8.346 19.7% 26.513 14.821 19.4 ST-Bi-LSTM 2.716 1.921 12.1 6.605 4.282 15.7 13.215 8.126 18.4% 25.133 15.036 18.9