Multiscale Temporal Features-Based Hybrid LSTM-GAT for Traffic Flow Prediction

Traffic flow prediction (TFP) plays a crucial role in optimizing road resource allocation and alleviating traffic congestion. However, existing TFP methods have limitations in capturing the complex spatiotemporal dependencies from traffic data, resulting in low prediction accuracy. To solve this problem, we propose a hybrid long short-term memory (LSTM) and graph attention network (GAT) model based on multiscale temporal features (MSTF-LG) to predict traffic flow. First, we employ trigonometric functions (TF) to process timestamp information to extract its periodicity and continuity features. These features are then integrated with traffic data to construct a comprehensive input representation. We further extract recent traffic data as well as daily, weekly, and monthly periodic data from this representation. Second, we adopt the LSTM encoder to process recent traffic data to extract recent trend features, and apply LSTM encoders to handle daily, weekly and monthly periodic data to extract periodic features. Furthermore, we employ the GAT to process these LSTM-encoded multiscale temporal features of traffic data to capture dynamic spatial characteristics. The normalized GAT outputs are fed into the LSTM decoder to effectively capture the dynamic temporal changes of traffic data, and then a linear layer transforms the output of the LSTM decoder into TFP results. Finally, experimental results demonstrate that the proposed method outperforms existing TFP methods in terms of prediction accuracy, robustness and computational efficiency.