SHORT-TERM TRAFFIC FLOW PREDICTION METHOD BASED ON CAUSAL GATED-LOW-PASS GRAPH CONVOLUTIONAL NETWORK

    公开(公告)号:US20240029556A1

    公开(公告)日:2024-01-25

    申请号:US18350347

    申请日:2023-07-11

    CPC classification number: G08G1/0133 G08G1/0129 G08G1/0145 G06N3/04

    Abstract: 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.

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