Abstract
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.
Highlights
With the development of sensors, computers and communication technologies, more and more high-speed and high-precision sensors are applied in the field of traffic detection
It is of great practical significance to carry out research on the fault data diagnosis technology of traffic detectors to effectively improve the reliability of intelligent transportation system (ITS) operations [3]
multi-scale principal component analysis (MSPCA) can more effectively resist the interference of noise, the false positives of fault are significantly reduced, but, due to the MSPCA in establishing the main space of atoms and the residual subspace being single, in dealing with traffic flow data, many false positives still appeared
Summary
With the development of sensors, computers and communication technologies, more and more high-speed and high-precision sensors are applied in the field of traffic detection. The projection space statistical eigenvectors are orthogonal to each other, which eliminates the correlation between variables and greatly reduces the complexity of the original process characteristic analysis Given their importance, the fault data detection of traffic detectors is always a research focus and has attracted the attention of numerous scholars for years [11]. The abnormal values are detected by the relationship between neighborhoods of data points, and the ST (space-time) signal of traffic video is transformed into a two-dimensional vector by principal component analysis (PCA). (1) Aiming at the fault problem of dynamic traffic data, a fault data detection model based on principal component analysis (PCA) is proposed. The fourth section tests the fault data recognition model based on the actual detector data and validates its effectiveness; Section 5 concludes the paper
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