基于自适应门控图神经网络的交通流预测

raffic flow prediction is an important part of intelligent transportation system. Due to the complexity of traffic data, long-term and accurate traffic flow prediction has always been one of the most challenging tasks in time series forecasting. In recent years, researchers have applied spatial-temporal graph modeling methods based on graph neural networks to traffic flow prediction tasks which achieved good prediction performance. However, existing graph modeling methods only reflect the spatial dependence in road networks through predefined adjacency structures, ignoring the importance of time-series correlation between nodes for prediction. Aiming at this limitation, this paper proposed an Adaptive Gated Graph Neural Network (Ada-GGNN) , the core of which was to simultaneously capture the spatial structure of the road network and adaptive time series correlation through the spatial passing module, and learned time-series features on nodes through the gating mechanism. The experimental results on two real-world traffic network datasets PeMSD7 and Los-loop show that the model has better performance.