Deformable Dynamic Convolution for Accurate yet Efficient Spatio-Temporal Traffic Prediction

Abstract: Spatio-temporal traffic prediction plays a key role in intelligent transportation systems by enabling accurate prediction in complex urban areas. Although not only accuracy but also efficiency for scalability is important, some previous methods struggle to capture heterogeneity such as varying traffic patterns across regions and time periods. Moreover, Graph Neural Networks (GNNs), which are the mainstream of traffic prediction, not only require predefined adjacency matrix, but also limit scalability to large-scale data containing many nodes due to their inherent complexity. To overcome these limitations, we propose Deformable Dynamic Convolution Network (DDCN) for accurate yet efficient traffic prediction. Traditional Convolutional Neural Networks (CNNs) are limited in modeling non-Euclidean spatial structures and spatio-temporal heterogeneity, DDCN overcomes these challenges by dynamically applying deformable filters based on offset. Specifically, DDCN decomposes transformer-style CNN to encoder-decoder structure, and applies proposed approaches to the spatial and spatio-temporal attention blocks of the encoder to emphasize important features. The decoder, composed of feed-forward module, complements the output of the encoder. This novel structure make DDCN can perform accurate yet efficient traffic prediction. In comprehensive experiments on four real-world datasets, DDCN achieves competitive performance, emphasizing the potential and effectiveness of CNN-based approaches for spatio-temporal traffic prediction.