Abstract: Traffic flow forecasting is of great importance to urban road planning,traffic safety issues and building smart cities.However,most existing traffic prediction models cannot capture the dynamic spatio-temporal correlation of traffic data well enough to obtain satisfactory prediction results.To address this problem,a dynamic spatio-temporal neural network-based city traffic flow prediction method is proposed to solve the traffic flow prediction problem.First,by modelling the nearest cycle dependence,daily cycle dependence and weekly cycle dependence of the traffic data,a 3D convolutional neural network is used on each component to extract the high-dimensional features of urban traffic.Then,an improved residual structure is used to capture the correlation between remote area pairs and the prediction area,and a fusion of spatial attention and temporal attention mechanisms is used to capture the dynamic correlation between traffic flows in different time periods in different areas.Finally,the outputs of the three components are weighted and fused using a parameter matrix-based approach to obtain the prediction results.Experiments on two publicly available datasets,TaxiBJ and BikeNYC,show that the proposed model outperforms the mainstream traffic forecasting models.

Key words: Temporal correlation, Traffic forecast, Traffic fluidity, Attention mechanism, Fusion mechanism