Fusion of RF algorithm and logistic regression model for high-speed illegal toll evasion vehicle inspection
17 mars 2025
À propos de cet article
Publié en ligne: 17 mars 2025
Reçu: 19 oct. 2024
Accepté: 15 févr. 2025
DOI: https://doi.org/10.2478/amns-2025-0182
Mots clés
© 2025 Haiyan Wu, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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Data details of various kinds of toll evasion behaviors
Type of fee evasion | Fee evasion | Data quantity |
---|---|---|
Imitation category | License plate does not match | 514 |
Cheating class | U-shape | 52 |
No card Gear shift | 64 | |
Change of weight | 96 | |
Overtime | 152 | |
Defect class | Excess weight | 78 |
There is no weight on the weighing table | 45 | |
Fight one’s way through a pass | 41 | |
Violation category | Outlet suspension shaft | 32 |
Inlet suspension shaft | 7 | |
Inlet weightless | 39 | |
Fee evasion | 17 |
Evaluation Table of inspection effect on TEVs
Models | Prediction accuracy | Classification accuracy | Audit time | MSE | RMSE | Stability |
---|---|---|---|---|---|---|
RF-logit | 0.88 | 0.87 | 0.86 | 0.89 | 0.77 | 0.85 |
RF-BPNN | 0.85 | 0.84 | 0.83 | 0.86 | 0.84 | 0.83 |
RF+SVM | 0.87 | 0.86 | 0.85 | 0.88 | 0.86 | 0.87 |
GBM | 0.74 | 0.83 | 0.82 | 0.75 | 0.83 | 0.84 |
RF-logit-BPNN | 0.92 | 0.94 | 0.90 | 0.92 | 0.91 | 0.93 |