Traffic flow prediction based on combination of support vector machine and data denoising schemes

Jinjun Tang, Xinqiang Chen, Zheng Hu, Fang Zong, Chunyang Han and Leixiao Li

Physica A: Statistical Mechanics and its Applications, 2019, vol. 534, issue C

Abstract: Traffic flow prediction with high accuracy is definitely considered as one of most important parts in the Intelligent Transportation Systems. As interfering by some external factors, the raw traffic flow data containing noise may cause decline of prediction performance. This study proposes a prediction method by combining denoising schemes and support vector machine model to improve prediction accuracy. This study comprehensively evaluated the multi-step prediction performance of models with different denoising algorithms using the traffic volume data collected from three loop detectors located on highway in city of Minneapolis. In the prediction performance comparison, five denoising methods including EMD (Empirical Mode Decomposition), EEMD (Ensemble Empirical Mode Decomposition), MA (Moving Average), BW filter (Butterworth) and WL (Wavelet) are considered as candidates, specially, four wavelet types, coif (coiflet), db (daubechies), haar and sym (symlet), are further compared based on accuracy evaluation indicators. The prediction results show that the prediction results of the model combined with denoising algorithm are better that of the model without denoising strategy. Furthermore, the improvement of the EEMD on prediction performance is higher than other denoising algorithms, and WL method with db type achieves higher accuracy than other three types. Through comparing prediction accuracy of different denoising models, this study provides valuable suggestions for selecting the appropriate denoising approach for traffic flow prediction.

Keywords: Traffic flow prediction; Denoising algorithm; Support vector machine; Ensemble empirical mode decomposition (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:534:y:2019:i:c:s0378437119302262

DOI: 10.1016/j.physa.2019.03.007

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