Research Article
Computational Logistics for Container Terminal Handling Systems with Deep Learning
Table 2
Prediction deviation profile of liner berthing time for QRM-LBT-LTW by partial features.
| | Prediction deviation (hours) | Minimum of liners | Maximum of liners | Mean of liners | Median of liners | Mode of liners | SD of liners | Variance of liners | Quantitative proportion of liners (%) |
| | [0, 0.5] | 17.000 | 34.000 | 25.440 | 25.000 | 26.000 | 3.864 | 14.926 | 29.582 | | (0.5, 1] | 19.000 | 28.000 | 23.590 | 24.000 | 23.000 | 1.795 | 3.222 | 27.430 | | (1, 2] | 20.000 | 43.000 | 31.390 | 31.000 | 31.000 | 4.361 | 19.018 | 36.500 | | (2, 3] | 0.000 | 3.000 | 0.890 | 1.000 | 1.000 | 0.527 | 0.278 | 1.035 | | (3, 4] | 0.000 | 1.000 | 0.690 | 1.000 | 1.000 | 0.463 | 0.214 | 0.802 | | (4, 5] | 1.000 | 2.000 | 1.070 | 1.000 | 1.000 | 0.255 | 0.065 | 1.244 | | (5, +∞] | 2.000 | 3.000 | 2.930 | 3.000 | 3.000 | 0.255 | 0.065 | 3.407 |
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