分享:
基地联席主任金大卫教授合作论文《Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning》在国际智能交通领域学术期刊《IEEE Transactions on Intelligent Transportation Systems》发表。
《IEEE Transactions on Intelligent Transportation Systems》是智能交通和自动驾驶领域国际顶级期刊，影响因子6.492，中科院一区TOP期刊，重点收录应用于交通系统的信息技术设计、计算、处理、分析和控制等创新研究成果。
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.
Keywords: Traffic flow prediction, graph convolutional network, federated learning, community detection, horizontal local road network.
教师简介
金大卫，中南财经政法大学信息与安全工程学院院长，数字技术与现代金融学科创新引智基地联席主任，人工智能与法商研究所所长，文澜青年学者，全国计算机学会对外联络委员会理事。长期从事信息类课程教学和金融信息工程相关研究工作。主持国家社会科学基金项目、教育部人文 社科 基金项目、国家博士后基金项目、湖北省教育厅基金项目，中 央高校基本科研基金项 目10余项。发表SCI、SSCI、CSCD、CSSCI等权威期刊发表论文20余篇。