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Article

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

by 1, 1,2,*, 2,* and 1,2
1
School of Information Science and Technology, Nantong University, Nantong 226019, China
2
School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Christophe Chesneau
Symmetry 2022, 14(1), 33; https://doi.org/10.3390/sym14010033
Received: 21 November 2021 / Revised: 16 December 2021 / Accepted: 22 December 2021 / Published: 28 December 2021
(This article belongs to the Special Issue Symmetry in Statistics and Data Science)
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. View Full-Text
Keywords: urban traffic prediction; machine learning; deep learning; neural networks; convolutional neural network (CNN); long short-term memory (LSTM) urban traffic prediction; machine learning; deep learning; neural networks; convolutional neural network (CNN); long short-term memory (LSTM)
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MDPI and ACS Style

Bao, Y.-X.; Shi, Q.; Shen, Q.-Q.; Cao, Y. Spatial-Temporal 3D Residual Correlation Network for Urban Traffic Status Prediction. Symmetry 2022, 14, 33. https://doi.org/10.3390/sym14010033

AMA Style

Bao Y-X, Shi Q, Shen Q-Q, Cao Y. Spatial-Temporal 3D Residual Correlation Network for Urban Traffic Status Prediction. Symmetry. 2022; 14(1):33. https://doi.org/10.3390/sym14010033

Chicago/Turabian Style

Bao, Yin-Xin, Quan Shi, Qin-Qin Shen, and Yang Cao. 2022. "Spatial-Temporal 3D Residual Correlation Network for Urban Traffic Status Prediction" Symmetry 14, no. 1: 33. https://doi.org/10.3390/sym14010033

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