Research Article
ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term Forecast in Rail Transit
Table 2
Forecast performance of ST-LSTM network on different stations.
| Station | ME | MAE | RMSE | MRE |
| 321 | 366.96 | 71.08 | 99.12 | 13.52% | 315 | 365.72 | 52.45 | 73.13 | 16.90% | 318 | 280.70 | 32.45 | 47.31 | 11.13% | 114 | 270.84 | 44.66 | 60.63 | 13.76% | 322 | 215.52 | 30.41 | 41.12 | 12.10% | 110 | 252.81 | 37.09 | 50.87 | 12.90% | 606 | 238.99 | 33.44 | 48.46 | 11.97% | 212 | 204.32 | 39.95 | 52.62 | 12.78% | 108 | 215.70 | 32.76 | 47.10 | 13.94% | 123 | 223.77 | 61.18 | 81.89 | 36.58% | | | | | | 335 | 69.81 | 6.58 | 10.10 | 29.34% | 221 | 43.85 | 6.83 | 9.07 | 28.39% | 305 | 58.25 | 9.55 | 12.93 | 32.25% | 620 | 67.57 | 6.46 | 9.59 | 27.07% | 621 | 45.15 | 6.69 | 8.91 | 30.47% | 224 | 31.58 | 5.99 | 7.98 | 32.93% | 219 | 36.12 | 5.40 | 7.40 | 31.86% | 619 | 35.96 | 4.77 | 6.56 | 29.32% | 118 | 23.63 | 4.41 | 5.72 | 29.88% | 625 | 49.00 | 5.47 | 8.04 | 35.59% |
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