Traffic Flow Prediction Using Multi-Scale Convolution and Attention Mechanisms

Traffic flow prediction is a critical task in intelligent transportation systems, significantly improving the efficiency of traffic management and scheduling. However, the complexity and diversity of traffic flow data pose substantial challenges to existing prediction methods. In particular, the frequent temporal variations and spatial characteristics in complex spatiotemporal data are difficult to handle effectively. To address this issue, this paper proposes MCANet, a novel prediction model designed to capture the intricate spatiotemporal features in traffic flow prediction through the integration of multi-scale convolution and attention mechanisms. Specifically, we introduce the Large Kernel Decomposition and Spatio-Temporal Selection LKD-STS module to enhance the model's ability to extract multi-scale features in traffic flow prediction, enabling it to better capture traffic patterns at different temporal scales. Additionally, we propose the Global Channel Spatial Attention GCSA module to improve the model's capability in capturing multi-scale traffic features and preserving spatial-channel information. Furthermore, we introduce the Partial Convolution Batch-normalization GELU PCBG module, which reduces redundant computations and memory access through partial convolution techniques, thereby enhancing the model's efficiency. Compared to the baseline model and other state-of-the-art SOTA traffic flow prediction models, MCANet demonstrates superior performance on the Flight and Traffic datasets. Notably, MCANet efficiently captures complex spatiotemporal features, maintaining stable performance in high-frequency traffic flow prediction tasks. Experimental results show that MCANet excels in traffic flow prediction tasks with varying prediction horizons. Particularly, for a prediction length of T=96, MCANet outperforms SOTA models such as MSGNet and TimesNet, with Mean Squared Error MSE reductions of 2.7 and 5.7 , respectively.