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Cited by 145Traffic Prediction and Management TechniquesTraffic control and managementPrivacy-Preserving Technologies in Data

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

Xia, Mengran, Jin, Dawei, Chen, Jingyu·2023·IEEE Transactions on Intelligent Transportation Systems
DOI 10.1109/tits.2022.3179391
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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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Show all 55 references

Research works printed but not linked: 4

  1. Gartner, 1982, Prescription for demand Responsive Urban Traffic Control, Transp. Res. Rec., 12, 73
  2. Defferrard, Convolutional neural networks on graphs with fast localized spectral filtering, 3844
  3. Kipf, Semi-supervised classification with graph convolutional networks, 1
  4. McMahan, Communication-efficient learning of deep networks from decentralized data, 54, 10

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Metadata

Authors
Xia, M., Jin, D., & Chen, J.
Affiliations
Zhongnan University of Economics and Law (CN)
ORCID
Venue
IEEE Transactions on Intelligent Transportation Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2023-01
Volume
24
Issue
1
Pages
1191-1203
ISSN
  • 1524-9050 (ISSN-L)
  • 1558-0016
DOI
10.1109/tits.2022.3179391
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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