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Volume 12, Issue 2
Stable Computation of Least Squares Problems of the OGM(1,N) Model and Short-Term Traffic Flow Prediction

Qin-Qin Shen, Yang Cao, Bo Zeng & Quan Shi

East Asian J. Appl. Math., 12 (2022), pp. 264-284.

Published online: 2022-02

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  • Abstract

The optimized grey multi-variable model, used to overcome the defects of the grey multi-variable model, is studied. Although this model represents a substantial improvement of the grey multi-variable one, unstable computation of the grey coefficients arising in ill-posed problems, may essentially diminish the model accuracy. Therefore, in the case of ill-posedness we employ regularization methods and use the generalized cross validation method to determine the regularization parameters. The methods developed are applied to the urban road short-term traffic flow prediction problem. Numerical simulations show that the methods proposed are highly accurate and outperform the grey multi-variate, the autoregressive integrated moving average, and the back propagation neural network models.

  • AMS Subject Headings

65F10, 62J05, 76A05, 91B84

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COPYRIGHT: © Global Science Press

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@Article{EAJAM-12-264, author = {Shen , Qin-QinCao , YangZeng , Bo and Shi , Quan}, title = {Stable Computation of Least Squares Problems of the OGM(1,N) Model and Short-Term Traffic Flow Prediction}, journal = {East Asian Journal on Applied Mathematics}, year = {2022}, volume = {12}, number = {2}, pages = {264--284}, abstract = {

The optimized grey multi-variable model, used to overcome the defects of the grey multi-variable model, is studied. Although this model represents a substantial improvement of the grey multi-variable one, unstable computation of the grey coefficients arising in ill-posed problems, may essentially diminish the model accuracy. Therefore, in the case of ill-posedness we employ regularization methods and use the generalized cross validation method to determine the regularization parameters. The methods developed are applied to the urban road short-term traffic flow prediction problem. Numerical simulations show that the methods proposed are highly accurate and outperform the grey multi-variate, the autoregressive integrated moving average, and the back propagation neural network models.

}, issn = {2079-7370}, doi = {https://doi.org/10.4208/eajam.280921.141121 }, url = {http://global-sci.org/intro/article_detail/eajam/20254.html} }
TY - JOUR T1 - Stable Computation of Least Squares Problems of the OGM(1,N) Model and Short-Term Traffic Flow Prediction AU - Shen , Qin-Qin AU - Cao , Yang AU - Zeng , Bo AU - Shi , Quan JO - East Asian Journal on Applied Mathematics VL - 2 SP - 264 EP - 284 PY - 2022 DA - 2022/02 SN - 12 DO - http://doi.org/10.4208/eajam.280921.141121 UR - https://global-sci.org/intro/article_detail/eajam/20254.html KW - Grey multi-variable model, least squares problem, ill-posed problem, regularization technique, traffic flow prediction. AB -

The optimized grey multi-variable model, used to overcome the defects of the grey multi-variable model, is studied. Although this model represents a substantial improvement of the grey multi-variable one, unstable computation of the grey coefficients arising in ill-posed problems, may essentially diminish the model accuracy. Therefore, in the case of ill-posedness we employ regularization methods and use the generalized cross validation method to determine the regularization parameters. The methods developed are applied to the urban road short-term traffic flow prediction problem. Numerical simulations show that the methods proposed are highly accurate and outperform the grey multi-variate, the autoregressive integrated moving average, and the back propagation neural network models.

Qin-Qin Shen, Yang Cao, Bo Zeng & Quan Shi. (2022). Stable Computation of Least Squares Problems of the OGM(1,N) Model and Short-Term Traffic Flow Prediction. East Asian Journal on Applied Mathematics. 12 (2). 264-284. doi:10.4208/eajam.280921.141121
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