LAUSR

Abstract Accurate and timely traffic forecasting is significant for intelligent transportation management. However, existing approaches model the temporal and spatial features of traffic flow inadequately. To address these limitations, a novel deep learning traffic forecasting framework based on graph attention network (GAT) and temporal convolutional network (TCN) is presented in this paper, termed as graph attention temporal convolutional networks (GATCN). More specifically, GATCN deal with the spatial features by GAT, and the temporal features by TCN. The layer fused by GAT and TCN enables the proposed model to learn the spatio-temporal characteristics that lie in traffic flow, while considering exogenous factors. In addition, nodes in the graph can capture the information of their neighborhoods by stacking multiple layers. Precision and robustness of the proposed method have been evaluated through testing on the real-world dataset. Results show that the proposed model outperforms other baselines.