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 - <p style="text-align: justify;">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.</p>