Global-Local Temporal Convolutional Network for Traffic Flow Prediction

Ren, Yajie; Zhang, Dong; Luo, Dan; Ma, Huadóng; Duan, Pengrui

doi:10.1109/tits.2020.3025076

Cited by 31 publications

(13 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experiments have been performed based on the steps outlined in Section 4 . The performance of the proposed GL-STRCN model is compared with that of four baseline models, e.g., CNN [ 26 ], ST-ResNet [ 38 ], GL-TCN [ 41 ], and DGLSTNet [ 42 ]. In particular, the global-local features of the collected data have been analyzed separately.…”

Section: Resultsmentioning

confidence: 99%

“…In order to consider the impact of global spatial features on traffic data, Ren et al [ 41 ] proposed global-local temporal convolutional network (GL-TCN) to capture global and local dynamics, but they ignored the analysis of data correlation in their work. Feng et al [ 42 ] proposed Dynamic Global-Local Spatial-Temporal Network (DGLSTNet) to derive the global and local information simultaneously from both spatial and temporal perspectives, but they ignored the capture of long-term temporal features.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Bao

Cao

Shen

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…They divided the city into raster matrix, and used convolution operation with residual units to improve the spatial-temporal dependence. Ren et al [39] considered the global and local spatial features respectively in the original spatial feature extraction. Guo et al [40] introduced external factors such as weather and holidays on the basis of ST-3DNet to capture the dynamic characteristics.…”

Section: Spatial-temporal Traffic Information Predictionmentioning

confidence: 99%

“…et al [39] considered the global and local spatial features respectively in the original spatial feature extraction. Guo et al [40] introduced external factors such as weather and holidays on the basis of ST-3DNet to capture the dynamic characteristics.…”

Section: Definition Of Traffic Raster Datamentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

“…(1) Current studies focus on the study of local spatial correlation [12]. Suppose one sensor represents one node, local spatial correlation means that the algorithm only considers the spatial correlation between directly adjacent or 2 nd order adjacent nodes [13]. However, spatial correlation also exists in a more extensive transportation network.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model Integrating Local and Global Spatial Correlation for Traffic Prediction

et al. 2022

View full text Add to dashboard Cite

Accurate traffic prediction can effectively alleviate traffic congestion problems. The complex spatial correlation of traffic flow contributes to the challenging prediction problem. Most current prediction methods focus on learning local spatial correlation, ignoring the spatial correlation of long-distance traffic flow. In this paper, we combine the improved Graph Convolutional Network (GCN) with Gated Recurrent Unit (GRU) to propose a hybrid model integrating local and global spatial correlation (T-LGGCN) for traffic prediction. The model consists of two parts: global spatial-temporal component and local spatial-temporal component. For the global spatial-temporal component, we construct the global correlation matrix to improve the GCN for obtaining the global spatial correlation. And GRU is stacked to obtain the global spatial-temporal correlation. For the local spatial-temporal component, we utilize the strategy of combining Fully Connected Layer (FCL) and GCN to analyze the local spatial correlation. Similarly, GRU is used to perform the output of local spatial-temporal correlation. The output of the two components is finally summed, and the prediction results are generated with the dense network. Experiments were conducted using the highway datasets PEMS04 and PEMS08 from the Caltrans Performance Measurement System, and the results show that our model significantly outperforms state-of-the-art baselines.

show abstract

Global-Local Temporal Convolutional Network for Traffic Flow Prediction

Cited by 31 publications

References 27 publications

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

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

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

A Hybrid Model Integrating Local and Global Spatial Correlation for Traffic Prediction

Contact Info

Product

Resources

About