2023/07/07 by Clyde Zhengdao Li, Wei Li, Li, Zhengdao +3
Computer Science · Engineering · Neuroscience · #Brain Tumor Detection and Classification #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural Networks and Applications #Traffic Prediction and Management Techniques
paper · pdf · doi:10.48550/arxiv.2307.05517
openalex publication_date 2023/07/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Traffic flow forecasting is a highly challenging task due to the dynamic spatial-temporal road conditions. Graph neural networks (GNN) has been widely applied in this task. However, most of these GNNs ignore the effects of time-varying road conditions due to the fixed range of the convolution receptive field. In this paper, we propose a novel Adaptive Graph Convolution Networks (AGC-net) to address this issue in GNN. The AGC-net is constructed by the Adaptive Graph Convolution (AGC) based on a novel context attention mechanism, which consists of a set of graph wavelets with various learnable scales. The AGC transforms the spatial graph representations into time-sensitive features considering the temporal context. Moreover, a shifted graph convolution kernel is designed to enhance the AGC, which attempts to correct the deviations caused by inaccurate topology. Experimental results on two public traffic datasets demonstrate the effectiveness of the AGC-net\footnoteCode is available at: https://github.com/zhengdaoli/AGC-net which outperforms other baseline models significantly.