HSFE: A Hierarchical Spatial-temporal Feature Enhanced Framework for Traffic Flow Forecasting

Currently, spatio-temporal fusion strategy is a key direction in traffic flow prediction. Current work focuses on the intrinsic spatio-temporal dependence of traffic flow data but often ignores the correlation between its actual features, dynamic changes, and other features, such as road occupancy rates and vehicle speeds. This can lead to problems such as the complexity of feature computation. This paper proposes a new framework for predicting traffic flow that enhances spatio-temporal features at multiple levels. The framework includes a periodic embedding module that captures temporal periodicity and encodes input data into more representative feature vectors for model training. Also, a component for fusing parallel channel attention has been designed to adaptively weigh the aggregation of features from global, local, and aggregated channels. This enhances the attention given to important feature information in the model. In addition, a multilevel sequential feature fusion enhancer has been designed that ensures feature processing at different levels. Experimental results on four public transportation datasets demonstrate that the innovative approach enhances the MAE metrics by an average of 2.50%, respectively, over all metrics in the baseline models. Notably, it reduces training time by approximately 50%. This paper also discusses ablation experiments to evaluate the performance of each module.