Abstract: Urban flow prediction plays a critical role in smart city research,providing essential data for urban planning and resource optimization.In recent years,Graph Neural Network (GNN)-based models have significantly enhanced the accuracy of urban flow prediction.However,most existing studies assume that training and testing data come from the same distribution,ignoring the complexity of dynamic changes in urban flow distribution in the real world,leading to poor model performance.To address this challenge,this paper proposes an urban flow prediction method based on the structural causal model to effectively deal with the challenge of model generalization caused by distribution shift.This method first utilizes a structural causal model to uncover the impact of environmental factors as confounders on flow prediction.It then designs a shared distribution estimator to learn the prior distribution of environmental information.Furthermore,a backdoor adjustment approach is introduced,combined with variational inference,to effectively eliminate the confounding effects caused by environmental factors.The proposed method can fairly consider different environmental factors,improving the accuracy and robustness of prediction.Experimental results on two real-world datasets show that the proposed model has high prediction accuracy and robustness when dealing with distribution shift.Compared with the six state-of-the-art baselines,the prediction performance is improved by 2.26%~9.18%.

Key words: Urban flow prediction,Causal inference,Distribution shift,Spatio-temporal data mining,Structural causal model