Traffic Flow Anomaly Detection Based on Wavelet Denoising and Support Vector Regression

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...

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Bibliographic Details
Main Authors: Jian Wu, Zhiming Cui, Yujie Shi, Dongliang Su
Format: Article
Language:English
Published: SAGE Publishing 2013-06-01
Series:Journal of Algorithms & Computational Technology
Online Access:https://doi.org/10.1260/1748-3018.7.2.209
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spelling doaj-a74102ea2bde43e39bb64ae4a71cec442020-11-25T04:01:00ZengSAGE PublishingJournal of Algorithms & Computational Technology1748-30181748-30262013-06-01710.1260/1748-3018.7.2.209Traffic Flow Anomaly Detection Based on Wavelet Denoising and Support Vector RegressionJian WuZhiming CuiYujie ShiDongliang SuIn 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.https://doi.org/10.1260/1748-3018.7.2.209
collection DOAJ
language English
format Article
sources DOAJ
author Jian Wu
Zhiming Cui
Yujie Shi
Dongliang Su
spellingShingle Jian Wu
Zhiming Cui
Yujie Shi
Dongliang Su
Traffic Flow Anomaly Detection Based on Wavelet Denoising and Support Vector Regression
Journal of Algorithms & Computational Technology
author_facet Jian Wu
Zhiming Cui
Yujie Shi
Dongliang Su
author_sort Jian Wu
title Traffic Flow Anomaly Detection Based on Wavelet Denoising and Support Vector Regression
title_short Traffic Flow Anomaly Detection Based on Wavelet Denoising and Support Vector Regression
title_full Traffic Flow Anomaly Detection Based on Wavelet Denoising and Support Vector Regression
title_fullStr Traffic Flow Anomaly Detection Based on Wavelet Denoising and Support Vector Regression
title_full_unstemmed Traffic Flow Anomaly Detection Based on Wavelet Denoising and Support Vector Regression
title_sort traffic flow anomaly detection based on wavelet denoising and support vector regression
publisher SAGE Publishing
series Journal of Algorithms & Computational Technology
issn 1748-3018
1748-3026
publishDate 2013-06-01
description 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.
url https://doi.org/10.1260/1748-3018.7.2.209
work_keys_str_mv AT jianwu trafficflowanomalydetectionbasedonwaveletdenoisingandsupportvectorregression
AT zhimingcui trafficflowanomalydetectionbasedonwaveletdenoisingandsupportvectorregression
AT yujieshi trafficflowanomalydetectionbasedonwaveletdenoisingandsupportvectorregression
AT dongliangsu trafficflowanomalydetectionbasedonwaveletdenoisingandsupportvectorregression
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