Traffic Flow Anomaly Detection Based on Wavelet Denoising and Support Vector Regression
In order to improve the speed and accuracy of traffic flow anomaly detection in real-time traffic system, we proposed an anomaly detection algorithm which is based on wavelet denoising and support vector regression. Firstly, we use wavelet transform to decompose and restructure the sampled data, and...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2013-06-01
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| Series: | Journal of Algorithms & Computational Technology |
| Online Access: | https://doi.org/10.1260/1748-3018.7.2.209 |
| Summary: | In order to improve the speed and accuracy of traffic flow anomaly detection in real-time traffic system, we proposed an anomaly detection algorithm which is based on wavelet denoising and support vector regression. Firstly, we use wavelet transform to decompose and restructure the sampled data, and then apply support vector regression to data training. By fitting the obtained data, it can achieve dynamic prediction of traffic flow parameters. Through comparing the predictive values with the measured values of traffic flow parameters, we can achieve traffic anomaly detection. Experimental results show that the method proposed in this paper has a higher detection rate under the same false alarm rate. |
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| ISSN: | 1748-3018 1748-3026 |