An optimized discrete grey multi-variable convolution model and its applications

Shen, Qin-Qin; Cao, Yang; Yao, Lin‐Quan; Zhu, Zhongkui

doi:10.1007/s40314-021-01448-z

Cited by 16 publications

(13 citation statements)

References 31 publications

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“…The common model-driven methods include ARIMA [13][14][15][16], the Kalman filter model [17,18], the grey model [19], etc. Although the model-driven methods have simple algorithm and convenient calculation, it has the problem that it cannot reflect the randomness and nonlinearity if the system model is static.…”

Section: Traffic Information Predictionmentioning

confidence: 99%

Spatial-Temporal 3D Residual Correlation Network for Urban Traffic Status Prediction

et al. 2021

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Accurate traffic status prediction is of great importance to improve the security and reliability of the intelligent transportation system. However, urban traffic status prediction is a very challenging task due to the tight symmetry among the Human–Vehicle–Environment (HVE). The recently proposed spatial–temporal 3D convolutional neural network (ST-3DNet) effectively extracts both spatial and temporal characteristics in HVE, but ignores the essential long-term temporal characteristics and the symmetry of historical data. Therefore, a novel spatial–temporal 3D residual correlation network (ST-3DRCN) is proposed for urban traffic status prediction in this paper. The ST-3DRCN firstly introduces the Pearson correlation coefficient method to extract a high correlation between traffic data. Then, a dynamic spatial feature extraction component is constructed by using 3D convolution combined with residual units to capture dynamic spatial features. After that, based on the idea of long short-term memory (LSTM), a novel architectural unit is proposed to extract dynamic temporal features. Finally, the spatial and temporal features are fused to obtain the final prediction results. Experiments have been performed using two datasets from Chengdu, China (TaxiCD) and California, USA (PEMS-BAY). Taking the root mean square error (RMSE) as the evaluation index, the prediction accuracy of ST-3DRCN on TaxiCD dataset is 21.4%, 21.3%, 11.7%, 10.8%, 4.7%, 3.6% and 2.3% higher than LSTM, convolutional neural network (CNN), 3D-CNN, spatial–temporal residual network (ST-ResNet), spatial–temporal graph convolutional network (ST-GCN), dynamic global-local spatial–temporal network (DGLSTNet), and ST-3DNet, respectively.

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Section: Traffic Information Predictionmentioning

confidence: 99%

Spatial-Temporal 3D Residual Correlation Network for Urban Traffic Status Prediction

et al. 2021

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“…Unfortunately, as demonstrated by Tien [54] , GM(1,n) and GM(m,n) all lead to inaccurate modeling. Despite this, we still encounter some erroneous works based on the classical GM(1,n), but there are also many correct works on improved GM(1,n) such as the convolution integral model (GMC(1,n)) [54] , [55] , [56] . However, GMC(1,n) still has defects [55] .…”

Section: Introductionmentioning

confidence: 99%

“…Despite this, we still encounter some erroneous works based on the classical GM(1,n), but there are also many correct works on improved GM(1,n) such as the convolution integral model (GMC(1,n)) [54] , [55] , [56] . However, GMC(1,n) still has defects [55] . Thus, in order to improve the GMC(1,n), some authors recently proposed additional structural forms.…”

Section: Introductionmentioning

confidence: 99%

Forecasting petroleum products consumption in Cameroon's household sector using a sequential GMC(1,n) model optimized by genetic algorithms

Sapnken¹,

Ahmat²,

Boukar³

et al. 2022

Heliyon

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“…The former category is based on mathematical theory, which uses a small amount of data samples to determine model parameters. The common model-driven methods include Autoregressive Integrated Moving Average model (ARIMA) [ 7 ], Kalman filter model [ 8 ], and grey model [ 9 ]. However, these methods are generally presented by simplified and solidified model structure involving ideal hypothesis.…”

Section: Introductionmentioning

confidence: 99%

Global-Local Spatial-Temporal Residual Correlation Network for Urban Traffic Status Prediction

Bao

Cao

Shen

et al. 2022

Computational Intelligence and Neuroscience

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The recent proposed Spatial-Temporal Residual Network (ST-ResNet) model is an effective tool to extract both spatial and temporal characteristics and has been successfully applied to urban traffic status prediction. However, the ST-ResNet model only extracts the local spatial characteristics and ignores the very important global spatial characteristics. In this paper, a novel Global-Local Spatial-Temporal Residual Correlation Network (GL-STRCN) model is proposed for urban traffic status prediction to further improve the prediction accuracy of the existing ST-ResNet model. The GL-STRCN model firstly applies Pearson’s correlation coefficient method to extract high correlation series. Then, considering both global and local spatial properties, two components consisting of 2D convolution and residual operation are used to capture spatial features. After that, based on Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU), a novel long-term temporal feature extraction component is proposed to capture temporal features. Finally, the spatial and temporal features are aggregated together in a weighted way for final prediction. Experiments have also been performed using two datasets from TaxiCD and PEMS-BAY. The results indicated that the proposed model produces a better prediction performance compared with the results based on other baseline solutions, e.g., CNN, ST-ResNet, GL-TCN, and DGLSTNet.

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An optimized discrete grey multi-variable convolution model and its applications

Cited by 16 publications

References 31 publications

Spatial-Temporal 3D Residual Correlation Network for Urban Traffic Status Prediction

Spatial-Temporal 3D Residual Correlation Network for Urban Traffic Status Prediction

Forecasting petroleum products consumption in Cameroon's household sector using a sequential GMC(1,n) model optimized by genetic algorithms

Global-Local Spatial-Temporal Residual Correlation Network for Urban Traffic Status Prediction

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