华南理工大学学报（自然科学版）2025，Vol.53Issue(9)：31-47,17.DOI:10.12141/j.issn.1000-565X.250003
基于动态自适应门控图卷积网络的交通拥堵预测
Traffic Congestion Prediction Based on Dynamic Adaptive Gated Graph Convolutional Networks
摘要
Abstract
With the continual rise in the number of motor vehicles in urban areas,traffic congestion has become increasingly severe,adversely affecting environmental protection and urban operational efficiency.Consequently,it is of critical importance to accurately predict traffic congestion for traffic management and optimization.However,existing research still faces limitations in modeling the dynamic,time-varying characteristics of traffic flow and the complex interactions among road segments.To address these challenges,a gated spatiotemporal convolutional network model based on graph neural networks was proposed to more effectively capture and predict traffic congestion.Firstly,an improved K-means clustering algorithm was employed to divide the raw data into multiple congestion-state categories,which are then incorporated as auxiliary features to enhance feature representation.Next,a gated temporal convolutional network was introduced to capture the temporal properties and dynamic dependencies in traffic data,and a dynamic adaptive gated graph convolutional network was constructed to achieve feature fusion and dynamic weight allocation through a signal generation module and a dual-modulation mechanism,thereby facilitating effective extraction of spatiotemporal features.Finally,residual connections were incorporated to improve training stability,and skip connections were utilized to integrate multi-level and multi-scale features.Experimental results on real-world PeMS08 and PeMS04 datasets demonstrate that the proposed model achieves superior prediction accuracy compared with other baseline methods.关键词
交通拥堵预测/图神经网络/动态自适应门控/聚类算法/门控时间卷积网络Key words
traffic congestion prediction/graph neural network/dynamic adaptive gating/clustering algorithm/gated temporal convolutional network分类
信息技术与安全科学引用本文复制引用
王庆荣,高桓伊,朱昌锋,何润田,慕壮壮..基于动态自适应门控图卷积网络的交通拥堵预测[J].华南理工大学学报（自然科学版）,2025,53(9):31-47,17.基金项目
国家自然科学基金项目(72161024) （72161024）
甘肃省教育厅"双一流"重大研究项目(GSSYLXM-04)Supported by the National Natural Science Foundation of China(72161024)and the Major Research Project under the Double First-Class Initiative of Gansu Provincial Department of Education(GSSYLXM-04) （GSSYLXM-04）
