Professor Dawei Jin, the Co-Director of the Base, has published a collaborative paper titled "Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning" in IEEE Transactions on Intelligent Transportation Systems, 2022.

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.

Key Words: Traffic flow prediction, graph convolutional network, federated learning, community detection, horizontal local road network.

Link: Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning | IEEE Journals & Magazine | IEEE Xplore

Teacher Profile

Dawei Jin, Dean of the School of Information and Safety Engineering, Zhongnan University of Economics and Law, Co-director of the Innovation and Talent Base for Digital Technology and Finance, Director of the Institute of Artificial Intelligence and Legal Business, Wenlan Young scholar, member of the Liaison Committee of China Computer Society. He has been engaged in information course teaching and financial information engineering related research for a long time. He has presided over more than 10 projects of the National Social Science Fund, the Humanities and Social Science Fund of the Ministry of Education, the National Postdoctoral Fund, the Education Department of Hubei Province, and the basic research fund of central universities. He has published more than 20 papers in SCI, SSCI, CSCD, CSSCI and other authoritative journals.