MCSTA: Multi-dimensional Collaborative Spatial-Temporal Attention Model for Traffic Flow Prediction

Traffic flow prediction is critical for the effective management and public safety of modern cities however, it remains a challenging task. The intricate spatiotemporal dependencies in traffic data and the trade-off between computational efficiency and predictive accuracy in existing models have long been key challenges. To address these issues, we propose a novel attention-based model built upon the Transformer architecture, termed the Multi-dimensional Collaborative Spatial-Temporal Attention Model MCSTA . Our model introduces several innovations: first, we design a Lightweight Multi-dimensional Cooperative Enhanced Attention LMCEA mechanism to capture spatiotemporal relationships across multiple dimensions. Additionally, we propose Non-dimensionality Reduction Local Cross-Channel Attention NDLCCA , which leverages 1D convolution to model local cross-channel interactions while circumventing dimensionality reduction operations. This approach significantly reduces computational complexity, enhances the utilization of inter-channel information, accurately captures correlations among channels, and ultimately provides more discriminative feature representations. Experimental evaluations on two real-world traffic datasets demonstrate that MCSTA outperforms state-of-the-art SOTA models. Compared to the baseline model, our approach achieves RMSE reductions of 3.43 , 6.63 , and 13.01 on the NYCBike dataset and 6.13 , 7.24 , and 7.49 on the NYCTaxi dataset, respectively.