Metro Passenger Flow Prediction Model Using Adaptive Multi-view Fusion Graph Neural Network
To solve the problem of insufficient modeling of multi-view spatial interaction in metro stations by traditional methods,this study proposes an Adaptive Multi-view fusion Graph Neural Network Model(AMFGNN)to conduct spatial interaction modeling in metro stations short-term passenger flow prediction.In the spatial dimension,the model includes multiple partial views such as physical topology graph,line accessibility graph,spatial distance graph,etc.,and uses the graph attention networks(GAT)to learn the dynamic spatial interaction within a single view.Taking the single-view station as the central node,combined with the station in other views as neighbor nodes,this paper constructs a fused view is and uses the GAT is to learn the dynamic interaction between multiple views.In the time dimension,the gated recurrent unit neural network is used to learn the time-varying characteristics of station passenger flow.The experiments were conducted in the Chongqing metro network,and the prediction results of the outbound passenger flow of the entire network show that compared with the physical virtual combined graph network model(PVCGN)in the baseline,the AMFGNN can reduce the average absolute error and root mean square error of the network's outbound passenger flow respectively by 3.06%and 2.49%.The visualization results of attention scores between nodes in multi-views graph show that the multi-view modeling based on the GAT can adaptively and effectively integrate station spatial information extracted from different views graph.In addition,the analysis of the impact factors of AMFGNN model performance show that using structurally stable views graph such as physical topology and line accessibility as central nodes to build a fusion view graph can obtain a more accurate and stable prediction model.
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