A short-term traffic flow prediction method based on a causal gated-low-pass graph convolutional network can include constructing a causal gated-low-pass graph convolutional network, where the causal gated-low-pass graph convolutional network includes a causal gated-low-pass convolutional block. The causal gated-low-pass convolutional block is connected to a fully-connected output layer, the causal gated-low-pass convolutional block includes two causal gated linear units and a low-pass graph convolutional block, and the low-pass graph convolutional block is set between the causal gated linear units. The method can further include obtaining a traffic flow network diagram and a traffic flow value based on traffic flow data, using the traffic flow network diagram as input, and performing short-term traffic flow prediction by using the causal gated-low-pass graph convolutional network. The method can predict short-term traffic flow with high accuracy.
SHORT-TERM TRAFFIC FLOW PREDICTION METHOD BASED ON CAUSAL GATED-LOW-PASS GRAPH CONVOLUTIONAL NETWORK
2024-01-25
Patent
Electronic Resource
English
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