Prediction model for urban multi-attraction tourist flow based on spatiotemporal multi-graph convolution

Abstract

Accurate tourist flow forecast can provide important support and guarantee for scenic spot staff and tourists to make decisions or plans. Aiming at the current situation that most of the existing forecasting methods are tourist flow forecasting of a single scenic spot and cannot fully mine spatial dependency information between various scenic spots, this paper proposes AMGCN model, a multi-graph convolution-based tourist flow prediction model for multiple scenic spots. First, AMGCN defines different non-Euclidean correlations between attractions, encoded as different graphs respectively, and uses multi-graph convolution to extract spatial correlation features. Then, a channel-wise attention mechanism incorporating global spatial context information is used to improve the performance of a long short-term memory network (LSTM), which is used to learn temporal correlation features. AMGCN is evaluated on a real-world large-scale urban multi-spot tourist flow historical dataset, the results show that the model outperforms the baseline model on regression evaluation metrics such as MAE, RMSE, MAPE, and SMAPE.