Research Article
Data Imputation for Detected Traffic Volume of Freeway Using Regression of Multilayer Perceptron
| Imputation method | MAPE or RMSE | Urban road or freeway | Data sources |
| Principal component analysis (PCA) | 76∼84 [32] | Urban road | China | 14∼24 [31] | Freeway | America | Fuzzy rough set (FRS) | 4.768∼6.533 [33] | Freeway | China | 6.9766∼10.4998 [34] | Freeway | China | Tensor completion | 4.0893∼5.3544 [35] | Urban road | China | 10.3%∼12.71% [36] | Freeway | America | 7.3%∼19.98% [37] | Urban road | China | | 0.91%∼64.95% [38] | Urban road | China | Support vector machine (SVR) | 4∼14 [40] | Both | China | 5.7232% [41] | Urban road | China | Denoising stacked autoencoders (DSAE) | 13.9∼20.9 [42] | Freeway | America | Convolutional neural network (CNN) | ≤24 [44] | Freeway | America | Long short-term memory (LSTM) | 9.63∼17.54 [46] | Urban road | America | 1.927∼9.192 [47] | Freeway | America | Generative adversarial networks (GAN) | 3.66∼10.86 [48] | Urban road | China and America | Dual-stage error-corrected boosting regressor (GBR) | 1.39%∼6.08% [49] | Freeway | America | Spatiotemporal-PTD | 3.45∼8.35 [50] | Urban road | China |
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