Abstract: Traffic flow prediction plays a key role in intelligent transportation system.However,due to its complex spatial-temporal dependence and its uncertainty,research becomes extremely challenging.Some existing methods mainly use a single time series and input it into a recurrent neural network to capture temporal dependency.Moreover,most models only simply stack temporal modules and spatial modules,resulting in ineffective feature fusion.To address these issues,this paper proposes a multi-time scale spatial-temporal graph neural network model.The model divides the sequence data into three time-scale sequences,then puts sequence data into the ST-Blocks to extract the spatial-temporal features of data,and finally makes the prediction.In ST-Block,graph convolutional network and variant Transformer are used to capture spatial dependency and temporal dependency respectively,and the output feature of the two sub-modules are fused through a gate mechanism.A large number of experiments are conducted on two real data sets in this paper,including short-term and long-term prediction,and the results show the excellent performance of MTSTGNN model on the task of traffic flow prediction.

Key words: Attention mechanism, Gate mechanism, Graph convolutional network, Traffic flow prediction, Transformer