Urban traffic flow prediction based on multi-spatio-temporal feature fusion

Urban traffic flow exhibits non-linear characteristics and highly complex spatio-temporal correlations, posing a huge challenge to traffic flow prediction. To address this challenge, we constructed a novel Multi-Spatio-Temporal Feature Fusion (MSTFF) model. This model accurately depicts the spatio-temporal correlations of traffic flow from multiple spatio-temporal perspectives. It decomposes the non-linear traffic flow into stable trend and random fluctuation, and decomposes the topological graph of the road network into first-order adjacency, second-order in-degree and second-order out-degree relationship graphs which describe the connectivity, input and output relationship of the nodes in road network respectively, thus constructing multiple perspectives that can exhibit different spatio-temporal properties. For each perspective, we use the self-attention mechanism to learn the long-term spatio-temporal correlations in the traffic flow, and use convolutional neural networks to learn the short-term spatio-temporal correlations. By fusing the features of spatio-temporal dependencies from multiple perspectives, we explored the high-dimensional spatio-temporal features of traffic flow and predicted the future traffic flow. We also developed the traffic flow prediction algorithm based on MSTFF, verified its performance using four real-world datasets, and compared it with twelve baseline models in the past five years. Experimental results show that the performance of the MSTFF model far exceeds that of existing related models, with average improvement rates of 15.10%, 19.63%, and 13.59% respectively in terms of the three indicators of MAE, RMSE, and MAPE. • Constructed a multi-spatiotemporal perspective for studying the traffic flow feature. • Extracted spatio-temporal features by inheriting self-attention mechanism and CNNs. • Developed traffic flow prediction algorithm via multi-spatiotemporal feature fusion.