Intra‐City Tourism Flow Forecasting: A Novel Deep Learning Model

ABSTRACT Intra‐city tourism flow forecasting plays a critical part in urban destination management and planning. However, research on this issue is extremely inadequate because of the challenges of intra‐city tourism flow forecasting and the difficulty of obtaining data on intra‐city tourism flows. Therefore, this study aims to construct a novel deep learning model that integrates a graph attention network and long short‐term memory for the accurate prediction of intra‐city tourism flows. A study was conducted in Xiamen, China, to confirm the validity of the proposed model supported by taxi data. The results reveal that the proposed model is applicable to intra‐city tourism flow forecasting and outperforms popular benchmarks in terms of forecasting accuracy and robustness. At last, our model effectively obtains information on distribution and temporal fluctuation of tourism flows.