Fault Data Detection of Traffic Detector Based on Wavelet Packet in the Residual Subspace Associated with PCA
To improve the accuracy and efficiency of fault data identification of traffic detectors is crucial in order to decrease the probability of unexpected failures of the intelligent transportation system (ITS). Since convolutional fault data recognition based on traffic flow three-parameter law has a p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/17/3491 |
| _version_ | 1818562036681932800 |
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| author | Xiaolu Li Xi Zhang Peng Zhang Guangyu Zhu |
| author_facet | Xiaolu Li Xi Zhang Peng Zhang Guangyu Zhu |
| author_sort | Xiaolu Li |
| collection | DOAJ |
| description | To improve the accuracy and efficiency of fault data identification of traffic detectors is crucial in order to decrease the probability of unexpected failures of the intelligent transportation system (ITS). Since convolutional fault data recognition based on traffic flow three-parameter law has a poor capability for multiscale of fault data, PCA (principal component analysis) is adopted for traffic fault data identification. This paper proposes the fault data detection models based on the PCA model, MSPCA (multiscale principal component analysis) model and improved MSPCA model, respectively. In order to improve the recognition rate of traffic detectors’ fault data, the improved MSPCA model combines the wavelet packet energy analysis and PCA to achieve traffic detector data fault identification. On the basis of traditional MSPCA, wavelet packet multi-scale decomposition is used to get detailed information, and principal component analysis models are established on different scale matrices, and fault data are separated by wavelet packet energy difference. Through case analysis, the feasibility verification of traffic flow data identification method is carried out. The results show that the improved method proposed in this paper is effective for identifying traffic fault data. |
| first_indexed | 2024-12-14T00:58:27Z |
| format | Article |
| id | doaj.art-77749b9b876147efbcf23e301ebf4814 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-14T00:58:27Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-77749b9b876147efbcf23e301ebf48142022-12-21T23:23:25ZengMDPI AGApplied Sciences2076-34172019-08-01917349110.3390/app9173491app9173491Fault Data Detection of Traffic Detector Based on Wavelet Packet in the Residual Subspace Associated with PCAXiaolu Li0Xi Zhang1Peng Zhang2Guangyu Zhu3School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, ChinaBeijing Transport Institute, Beijing 10073, ChinaTransport Planning and Research Institute, Ministry of Transport, Beijing 100028, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, ChinaTo improve the accuracy and efficiency of fault data identification of traffic detectors is crucial in order to decrease the probability of unexpected failures of the intelligent transportation system (ITS). Since convolutional fault data recognition based on traffic flow three-parameter law has a poor capability for multiscale of fault data, PCA (principal component analysis) is adopted for traffic fault data identification. This paper proposes the fault data detection models based on the PCA model, MSPCA (multiscale principal component analysis) model and improved MSPCA model, respectively. In order to improve the recognition rate of traffic detectors’ fault data, the improved MSPCA model combines the wavelet packet energy analysis and PCA to achieve traffic detector data fault identification. On the basis of traditional MSPCA, wavelet packet multi-scale decomposition is used to get detailed information, and principal component analysis models are established on different scale matrices, and fault data are separated by wavelet packet energy difference. Through case analysis, the feasibility verification of traffic flow data identification method is carried out. The results show that the improved method proposed in this paper is effective for identifying traffic fault data.https://www.mdpi.com/2076-3417/9/17/3491traffic detectortraffic flow datafault data detectionwavelet packet energy analysisprincipal component analysis |
| spellingShingle | Xiaolu Li Xi Zhang Peng Zhang Guangyu Zhu Fault Data Detection of Traffic Detector Based on Wavelet Packet in the Residual Subspace Associated with PCA Applied Sciences traffic detector traffic flow data fault data detection wavelet packet energy analysis principal component analysis |
| title | Fault Data Detection of Traffic Detector Based on Wavelet Packet in the Residual Subspace Associated with PCA |
| title_full | Fault Data Detection of Traffic Detector Based on Wavelet Packet in the Residual Subspace Associated with PCA |
| title_fullStr | Fault Data Detection of Traffic Detector Based on Wavelet Packet in the Residual Subspace Associated with PCA |
| title_full_unstemmed | Fault Data Detection of Traffic Detector Based on Wavelet Packet in the Residual Subspace Associated with PCA |
| title_short | Fault Data Detection of Traffic Detector Based on Wavelet Packet in the Residual Subspace Associated with PCA |
| title_sort | fault data detection of traffic detector based on wavelet packet in the residual subspace associated with pca |
| topic | traffic detector traffic flow data fault data detection wavelet packet energy analysis principal component analysis |
| url | https://www.mdpi.com/2076-3417/9/17/3491 |
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