Research on traffic flow prediction model of urban hot spots
In the local and hot spots of the city,traffic prediction is significantly affected by the characteristics of the site itself.In view of the above problems,this paper proposes a traffic flow prediction model for the application environment of urban sites,which can combine the characteristics of the site itself to complete the feature analysis.The spatial and temporal features are extracted by the adaptive graph convolution(DGCN)module and the gated expansion causal convolution respectively,and the layer depth is increased to obtain a larger receptive field,and then a richer node time feature is obtained.The results of comparative experiments and ablation experiments show that the CS-DSTGCN prediction model has outstanding application value and advantages in traffic flow prediction in urban site environment.
urban traffic managementtraffic flowhotspot sitesprediction model
NETL
NSTL
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