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A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine

Author

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  • Qiang Shang
  • Ciyun Lin
  • Zhaosheng Yang
  • Qichun Bing
  • Xiyang Zhou

Abstract

Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.

Suggested Citation

  • Qiang Shang & Ciyun Lin & Zhaosheng Yang & Qichun Bing & Xiyang Zhou, 2016. "A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-25, August.
    • Handle: RePEc:plo:pone00:0161259
    DOI: 10.1371/journal.pone.0161259
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    References listed on IDEAS

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    Cited by:

    1. Wei Zhou & Wei Wang & Xuedong Hua & Yi Zhang, 2020. "Real-Time Traffic Flow Forecasting via a Novel Method Combining Periodic-Trend Decomposition," Sustainability, MDPI, vol. 12(15), pages 1-23, July.
    2. Alireza Ermagun & Snigdhansu Chatterjee & David Levinson, 2017. "Using temporal detrending to observe the spatial correlation of traffic," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-21, May.

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