Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning

Xia, Mengran; Jin, Dawei; Chen, Jingyu

doi:10.1109/tits.2022.3179391

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

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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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.
“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]

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Authors: Xia, M., Jin, D., & Chen, J.
Affiliations: Zhongnan University of Economics and Law (CN)
ORCID: Xia, Mengran
Jin, Dawei
Chen, Jingyu
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 · 2022: 1
FWCI: 11.70
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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: Physical Sciences › Engineering › Building and Construction › 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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Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning

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