Tourism demand forecasting of multi-attractions with spatiotemporal grid: a convolutional block attention module model

Abstract

Effective tourist demand forecasting is crucial for company operations and destination management. Furthermore, tourists may plan better personalized multi-attraction itineraries based on demand forecasting to avoid travel peaks and improve the enjoyment of their vacation. This study developed a unique deep learning model called the convolution block attention module (CBAM) that is built on convolutional blocks and attention modules to estimate tourism demand precisely. Then, the passenger flow grid map was extracted from mobile phone signaling data. To forecast the subsequent period of the passenger flow grid map, the CBAM model uses the multi-channel spatial-temporal grid graph that is built by multiple successive passenger flow grid maps. Finally, the forecasted passenger flow grid map was used to derive the tourist demand for multi-attractions for the next period. The analysis of mobile phone signaling data from Beijing and Xiamen using the proposed model reveals that its mean absolute percentage error (MAPE) is 8.11%, which is lower than other benchmark deep learning models.