Research Article
A Road Network Enhanced Gate Recurrent Unit Model for Gather Prediction in Smart Cities
Table 1
Evaluation of prediction results in terms of Recall@
and AUC on three datasets.
(a) |
| NPF | Recall@1 | Recall@5 | Recall@10 | Recall@20 | AUC | LSTM | 0.0428 | 0.1677 | 0.2894 | 0.4300 | 0.6951 | GRU | 0.0712 | 0.2372 | 0.3421 | 0.4771 | 0.7778 | RNN | 0.0809 | 0.2580 | 0.3570 | 0.4865 | 0.8018 | ST-LSTM | 0.0621 | 0.2438 | 0.3663 | 0.5050 | 0.7976 | STGN | 0.0762 | 0.2728 | 0.3734 | 0.5061 | 0.8177 | STGRU | 0.0920 | 0.2829 | 0.3891 | 0.5231 | 0.8290 |
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(b) |
| OPF | Recall@1 | Recall@5 | Recall@10 | Recall@20 | AUC | LSTM | 0.0383 | 0.1638 | 0.2634 | 0.4375 | 0.7140 | GRU | 0.0455 | 0.1979 | 0.3007 | 0.4656 | 0.7611 | RNN | 0.0588 | 0.2258 | 0.3260 | 0.4881 | 0.8324 | ST-LSTM | 0.0502 | 0.2107 | 0.3174 | 0.4890 | 0.7817 | STGN | 0.0633 | 0.1699 | 0.2833 | 0.4920 | 0.8292 | STGRU | 0.0681 | 0.2439 | 0.3464 | 0.5116 | 0.8545 |
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(c) |
| TPF | Recall@1 | Recall@5 | Recall@10 | Recall@20 | AUC | LSTM | 0.0752 | 0.3331 | 0.4849 | 0.6428 | 0.8317 | GRU | 0.0789 | 0.3319 | 0.4793 | 0.6384 | 0.8451 | RNN | 0.0856 | 0.3634 | 0.5066 | 0.6561 | 0.8645 | ST-LSTM | 0.0864 | 0.3699 | 0.5213 | 0.6682 | 0.8727 | STGN | 0.0900 | 0.3327 | 0.5103 | 0.6672 | 0.8734 | STGRU | 0.0933 | 0.3795 | 0.5263 | 0.6730 | 0.8755 |
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