Smart City Gnosys
Smart city article details
| Title | Spatiotemporal Dynamic Forecasting And Analysis Of Regional Traffic Flow In Urban Road Networks Using Deep Learning Convolutional Neural Network |
|---|---|
| ID_Doc | 52608 |
| Authors | Wu S. |
| Year | 2022 |
| Published | IEEE Transactions on Intelligent Transportation Systems, 23, 2 |
| DOI | http://dx.doi.org/10.1109/TITS.2021.3098461 |
| Abstract | The purpose is to explore the spatial-Temporal dynamic prediction performance of urban road network traffic flow based on convolutional neural networks (CNN) of deep learning. A dynamic prediction model of road network traffic flow based on STGCN-BiLSTM (spatial-Temporal graph convolution network Bi-directional Long Short-Term Memory) is designed in view of the complex and highly nonlinear traffic data in the actual environment. Finally, the simulation experiment is conducted on the constructed model to verify its performance which is compared with that of the LSTM (Long Short-Term Memory) model, CNN model, RNN (Recurrent Neural Network) model, AlexNet model and STGCN model. The results show that the root mean square error, mean absolute error and mean absolute percentage error of the proposed algorithm model are 4.60%, 5.46% and 7.73%, respectively. The training time is stable at about 45s, the test time is stable at about 33s, and the delay is close to 1.86\times 10 {-4}\text{s}. Additionally, the traffic flow of the test section in the next 15 min, 30 min, 1 h and 2 h is further predicted. Compared with other algorithms, the predicted value of the proposed algorithm is the closest to the actual value, along with the best prediction effect. Therefore, the constructed dynamic prediction model of road network traffic flow can achieve high accuracy and better robustness under the premise of low error, which can provide experimental basis for the spatial-Temporal dynamic digital development of transportation in smart cities. © 2000-2011 IEEE. |
| Author Keywords | BiLSTM; Convolutional neural network; deep learning; traffic flow; urban road network |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
58588  | 0.934 | Wang X. | Traffic Flow Prediction Method Based On Deep Learning And Graph Neural Network | International Journal of High Speed Electronics and Systems (2025) |
| 4879 | 0.929 | Li Y.; Zhao W.; Fan H. | A Spatio-Temporal Graph Neural Network Approach For Traffic Flow Prediction | Mathematics, 10, 10 (2022) |
| 52587 | 0.927 | Priya K.; Omkar K.; Naresh R.; Venkatesh K. | Spatio-Temporal Traffic Prediction In Urban Area Using Convolutional Temporal Network System | 6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Proceedings (2025) |
| 53045 | 0.926 | Wang C.; Wang L.; Wei S.; Sun Y.; Liu B.; Yan L. | Stn-Gcn: Spatial And Temporal Normalization Graph Convolutional Neural Networks For Traffic Flow Forecasting | Electronics (Switzerland), 12, 14 (2023) |
| 48709 | 0.925 | Zhu H.; Mao Y.; Zhao Y.; Shi L.; Shao Y. | Short-Term Traffic Flow Prediction Method Based On Spatio-Temporal Characteristics Of Complex Road Network | Advances in Transdisciplinary Engineering, 20 (2022) |
| 58576 | 0.924 | Zheng L.; Pu Y.; Sun W. | Traffic Flow Prediction Algorithm Based On Attention Spatiotemporal Graph Convolution Mechanism | IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (2024) |
| 52517 | 0.923 | Huang X.; Pan Z.; Zhao G. | Spatial-Temporal Interactive Graph Convolutional Networks For Traffic Forecasting | 2024 4th International Conference on Electronic Information Engineering and Computer Technology, EIECT 2024 (2024) |
| 18083 | 0.921 | Tang J.; Zhu R.; Wu F.; He X.; Huang J.; Zhou X.; Sun Y. | Deep Spatio-Temporal Dependent Convolutional Lstm Network For Traffic Flow Prediction | Scientific Reports, 15, 1 (2025) |
| 21123 | 0.92 | Hu J.; Lin X.; Wang C. | Dstgcn: Dynamic Spatial-Temporal Graph Convolutional Network For Traffic Prediction | IEEE Sensors Journal, 22, 13 (2022) |
| 29827 | 0.919 | Chen B.; Hu K.; Li Y.; Miao L. | Hybrid Spatio-Temporal Graph Convolution Network For Short-Term Traffic Forecasting | IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2022-October (2022) |