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Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method

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  • Cheng, Anyu
  • Jiang, Xiao
  • Li, Yongfu
  • Zhang, Chao
  • Zhu, Hao

Abstract

This study proposes a multiple sources and multiple measures based traffic flow prediction algorithm using the chaos theory and support vector regression method. In particular, first, the chaotic characteristics of traffic flow associated with the speed, occupancy, and flow are identified using the maximum Lyapunov exponent. Then, the phase space of multiple measures chaotic time series are reconstructed based on the phase space reconstruction theory and fused into a same multi-dimensional phase space using the Bayesian estimation theory. In addition, the support vector regression (SVR) model is designed to predict the traffic flow. Numerical experiments are performed using the data from multiple sources. The results show that, compared with the single measure, the proposed method has better performance for the short-term traffic flow prediction in terms of the accuracy and timeliness.

Suggested Citation

  • Cheng, Anyu & Jiang, Xiao & Li, Yongfu & Zhang, Chao & Zhu, Hao, 2017. "Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 422-434.
    • Handle: RePEc:eee:phsmap:v:466:y:2017:i:c:p:422-434
    DOI: 10.1016/j.physa.2016.09.041
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    References listed on IDEAS

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    Cited by:

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    7. Su-qi Zhang & Kuo-Ping Lin, 2020. "Short-Term Traffic Flow Forecasting Based on Data-Driven Model," Mathematics, MDPI, vol. 8(2), pages 1-17, January.
    8. Ling Shen & Jian Lu & Dongdong Geng & Ling Deng, 2020. "Peak Traffic Flow Predictions: Exploiting Toll Data from Large Expressway Networks," Sustainability, MDPI, vol. 13(1), pages 1-18, December.
    9. Huiming Duan & Xinping Xiao, 2019. "A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors," Complexity, Hindawi, vol. 2019, pages 1-18, June.
    10. Zhang, Weibin & Zha, Huazhu & Zhang, Shuai & Ma, Lei, 2023. "Road section traffic flow prediction method based on the traffic factor state network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    11. Huang, Hai-chao & He, Hong-di & Zhang, Zhe & Ma, Qing-hai & Xue, Xing-kuo & Zhang, Wen-xiu, 2024. "Variable-length traffic state prediction and applications for urban network with adaptive signal timing plan," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    12. Liu, Qingchao & Cai, Yingfeng & Jiang, Haobin & Lu, Jian & Chen, Long, 2018. "Traffic state prediction using ISOMAP manifold learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 532-541.

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