###### Abstract  
An essential component of autonomous transportation system management and decision-making is precise and real-time traffic flow forecast. Predicting future traffic conditionsis a difficult undertaking because of the intricate spatio-temporal relationships involved. Existing techniques often employ separate modules to model spatio-temporal features independently, thereby neglecting the temporally and spatially heterogeneous features among nodes. Simultaneously, many existing methods overlook the long-term relationships included in traffic data, subsequently impacting prediction accuracy. We introduce a novel method to traffic flow forecasting based on the combination of the feature-augmented down-sampling dynamic graph convolutional network and multi-head attention mechanism. Our method presents a feature augmentation mechanism to integrate traffic data features at different scales. The subsampled convolutional network enhances information interaction in spatio-temporal data, and the dynamic graph convolutional network utilizes the generated graph structure to better simulate the dynamic relationships between nodes, enhancing the model's capacity for capturing spatial heterogeneity. Through the feature-enhanced subsampled dynamic graph convolutional network, the model can simultaneously capture spatio-temporal dependencies, and coupled with the process of multi-head temporal attention, it achieves long-term traffic flow forecasting. The findings demonstrate that the ADDGCN model demonstrates superior prediction capabilities on two real datasets (PEMS04 and PENS08). Notably, for the PEMS04 dataset, compared to the best baseline, the performance of ADDGCN is improved by 2.46% in MAE and 2.90% in RMSE; for the PEMS08 dataset, compared to the best baseline, the ADDGCN performance is improved by 1.50% in RMSE, 3.46% in MAE, and 0.21% in MAPE, indicating our method's superior performance. spatio-temporal correlation; feature augmentation; downsampling convolution; dynamic graph convolution; multi-head attention +
Footnote †: journal: Medical  
like flow rate and speed by using past traffic records [2]. However, traffic flow predicting is complex because of intricate spatio-temporal dependencies, presenting significant challenges for accurate and real-time predictions. Unlike traditional time series analysis, traffic forecasting is influenced by various external factors, including spatial factors [3]. As shown in Figure 1, in the temporal dimension, the mobility behaviors of urban residents result in clear cyclical patterns in overall traffic flow [4]. Moreover, traffic flows are susceptible to volatility caused by external variables such as events or traffic accidents, which cannot be avoided. In the spatial dimension, traffic information propagates through the structure of the road system. For instance, the flow of traffic on particular roads directly affects the movement of vehicles on adjacent roads, highlighting the transient and dynamic nature of traffic [5]. Spatio-temporal correlation also exhibits multi-scale characteristics, encompassing global and local features in spatial dimensions and long-term and short-term dynamics in temporal dimensions [6]. These characteristics have significant implications for understanding and predicting traffic flow changes. Deep learning techniques have been extensively employed in recent years to investigate the spatio-temporal relationships of traffic data. Graph neural networks (GNNs) [7] have provided a viable approach for modeling non-Euclidean spatio-temporal networks. Models like STGCN [8], T-GCN [9] and DCRNN [10] combine recurrent neural networks with graph convolutional networks to model spatio-temporal relationships. STGNN [11] introduces GRU and Transformer architectures to handle local and global temporal correlations. However, these approaches rely on prior knowledge of the graph structure [12] and fail to fully account for dynamic changes and heterogeneity in spatio-temporal data.