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A noise-immune Kalman filter for short-term traffic flow forecasting

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Listed:
  • Cai, Lingru
  • Zhang, Zhanchang
  • Yang, Junjie
  • Yu, Yidan
  • Zhou, Teng
  • Qin, Jing

Abstract

This paper formulates the traffic flow forecasting task by introducing a maximum correntropy deduced Kalman filter. The traditional Kalman filter is based on minimum mean square error, which performs well under Gaussian noises. However, the real traffic flow data are fulfilled with non-Gaussian noises. The traditional Kalman filter may rot under this situation. The Kalman filter deduced by maximum correntropy criteria is insensitive to non-Gaussian noises, meanwhile retains the optimal state mean and covariance propagation of the traditional Kalman filter. To achieve this, a fix-point algorithm is embedded to update the posterior estimations of maximum correntropy deduced Kalman filter. Extensive experiments on four benchmark datasets demonstrate the outperformance of this model for traffic flow forecasting.

Suggested Citation

  • Cai, Lingru & Zhang, Zhanchang & Yang, Junjie & Yu, Yidan & Zhou, Teng & Qin, Jing, 2019. "A noise-immune Kalman filter for short-term traffic flow forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    • Handle: RePEc:eee:phsmap:v:536:y:2019:i:c:s0378437119314876
    DOI: 10.1016/j.physa.2019.122601
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    Cited by:

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    2. Chen, Xinqiang & Chen, Huixing & Yang, Yongsheng & Wu, Huafeng & Zhang, Wenhui & Zhao, Jiansen & Xiong, Yong, 2021. "Traffic flow prediction by an ensemble framework with data denoising and deep learning model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    3. Zhanzhong Wang & Ruijuan Chu & Minghang Zhang & Xiaochao Wang & Siliang Luan, 2020. "An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning," Sustainability, MDPI, vol. 12(20), pages 1-22, October.
    4. Liu, Yang & Song, Yaolun & Zhang, Yan & Liao, Zhifang, 2022. "WT-2DCNN: A convolutional neural network traffic flow prediction model based on wavelet reconstruction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    5. Fang, Weiwei & Zhuo, Wenhao & Yan, Jingwen & Song, Youyi & Jiang, Dazhi & Zhou, Teng, 2022. "Attention meets long short-term memory: A deep learning network for traffic flow forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    6. Ma, Changxi & Zhang, Bowen & Li, Shukai & Lu, Youpeng, 2024. "Urban rail transit passenger flow prediction with ResCNN-GRU based on self-attention mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    7. Tian, Jing & Song, Xianmin & Tao, Pengfei & Liang, Jiahui, 2022. "Pattern-adaptive generative adversarial network with sparse data for traffic state estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    8. Yulong Pei & Songmin Ran & Wanjiao Wang & Chuntong Dong, 2023. "Bus-Passenger-Flow Prediction Model Based on WPD, Attention Mechanism, and Bi-LSTM," Sustainability, MDPI, vol. 15(20), pages 1-20, October.
    9. Chih-Yao Chang & Kuo-Ping Lin, 2020. "Developing Support Vector Machine with New Fuzzy Selection for the Infringement of a Patent Rights Problem," Mathematics, MDPI, vol. 8(8), pages 1-16, August.
    10. Huang, Haichao & Chen, Jingya & Sun, Rui & Wang, Shuang, 2022. "Short-term traffic prediction based on time series decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    11. Wenguang Chai & Yuexin Zheng & Lin Tian & Jing Qin & Teng Zhou, 2023. "GA-KELM: Genetic-Algorithm-Improved Kernel Extreme Learning Machine for Traffic Flow Forecasting," Mathematics, MDPI, vol. 11(16), pages 1-15, August.
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    13. Fei Wang & Yinxi Liang & Zhizhe Lin & Jinglin Zhou & Teng Zhou, 2024. "SSA-ELM: A Hybrid Learning Model for Short-Term Traffic Flow Forecasting," Mathematics, MDPI, vol. 12(12), pages 1-17, June.

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