The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning
Abstract
This study proposes a short-term traffic flow prediction model that combines community detection-based federated learning with a graph convolutional network (GCN) to alleviate the time-consuming training, higher communication costs, and data privacy risks of global GCNs as the amount of data increases. The federated community GCN (FCGCN) can achieve timely, accurate, and safe traffic state predictions in the era of big traffic data, which is critical for the efficient operation of intelligent transportation systems. The FCGCN prediction process has four steps: dividing the local subnetwork with community detection, local training based on the global parameters, uploading the local model parameters, and constructing a global model prediction based on the aggregated parameters. Numerical results on the PeMS04 and PeMS08 datasets show that the FCGCN outperforms four benchmark models, namely, the long short-term memory (LSTM), convolutional neural network (CNN), ChebNet, and graph attention network (GAT) models. The FCGCN prediction is closer to the real value, with nearly the same performance as the global model at a lower time cost, thus achieving accurate and secure short-term traffic flow predictions with three parameters: flow, speed, and occupancy.
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“FL for traffic flow prediction proposed in [156] replaced the centralized road network model with a decentralized global GCN to balance accuracy and computational cost.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [156]
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Semi-supervised classification with graph convolutional networks
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Graph Attention NetworksPreprint
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McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2017) · arXiv · doi:10.48550/arxiv.1710.10903 · Cited by 12,107
Advances In Deep Learning On Graphs (Gsp'18 Workshop)Preprint
Defferrard, M., Bresson, X., & Vandergheynst, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.09375 · Cited by 7,021
Federated Machine Learning: Concept and Applications
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Federated Learning: Strategies for Improving Communication Efficiency
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Cited by
Sorted by citations
Graph Neural Network for Traffic Forecasting: The Research Progress
Jiang, W., Luo, J., He, M., & Gu, W. (2023) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi12030100 · Cited by 225
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems
Zhang, S., Li, J., Shi, L. X., Ding, M., Nguyen, D. C., Tan, W., Weng, J., & Han, Z. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3324962 · Cited by 182
A Survey on Federated Learning in Intelligent Transportation Systems
Zhang, R., Mao, J., Wang, H., Li, B., Cheng, X., & Yang, L. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3446319 · Cited by 107
A comprehensive survey of federated transfer learning: challenges, methods and applications
Guo, W., Zhuang, F., Zhang, X., Tong, Y., & Dong, J. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-024-40065-x · Cited by 95
Blika, A., Palmos, S., Doukas, G. S., Lamprou, V., Pelekis, S., Kontoulis, M., Ntanos, C., & Askounis, D. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3449563 · Cited by 82
Metadata
- Authors
- Xia, M., Jin, D., & Chen, J.
- Affiliations
- Zhongnan University of Economics and Law (CN)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2023-01
- Volume
- 24
- Issue
- 1
- Pages
- 1191-1203
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W4285260582
- Cited by
- 142
- References
- 55
- Citations by year
- 2026: 35 · 2025: 50 · 2024: 38 · 2023: 18
- FWCI
- 11.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- Funders
- Innovation and Talent Base for Digital Technology and Finance (B21038)
- Fundamental Research Funds for the Central Universities, Zhongnan University of Economics and Law (2722021AJ001)
- Talent Training Program for Graduate Students of Zhongnan University of Economics and Law (YRTD202214, XKRH202101)
- Topics
- Social Sciences › Transportation › Traffic Prediction and Management Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- short-term traffic forecasting, graph convolutional networks, federated learning, community detection, traffic state prediction, data privacy, intelligent transportation systems
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