Multi-scale Fusion Dynamic Graph Neural Network For Traffic Flow Prediction

As a cornerstone of intelligent transportation systems, traffic flow prediction has garnered extensive research attention. However, traffic flow data exhibits pronounced spatio-temporal dynamics, rendering accurate traffic flow prediction a challenging endeavor. Existing methodologies compartmentalize the capture of temporal and spatial dependencies through distinct modules, overlooking latent spatio-temporal heterogeneity within traffic data. Furthermore, they often solely consider spatio-temporal correlations between adjacent historical time steps, neglecting the extraction of temporal dependencies across different time intervals. To address these issues, we propose a novel Multi-scale Fusion Dynamic Graph Neural Network (MFDGNN). MFDGNN employs Temporal Convolutional Networks (TCNs) to generate time series on multiple scales, subsequently extracting spatio-temporal features from these sequences to construct mutually independent fusion graphs. By leveraging fusion graphs to extract features from time series on multiple scales, our model adeptly captures spatio-temporal dependencies of traffic nodes across various time dimensions, thereby achieving enhanced precision in traffic flow prediction. Experimental results conducted on four publicly available datasets demonstrate the superiority of our model compared to other baseline approaches.