Forecasting Inter-Destination Tourism Flow via a Hybrid Deep Learning Model

TL;DR

This paper introduces a hybrid deep learning model that predicts inter-destination tourism flow using multi-source data, enhancing tourism management and understanding tourists' destination choices.

Contribution

It presents a novel graph-based hybrid deep learning approach that incorporates explicit attraction features and implicit interactions, addressing data limitations and privacy issues.

Findings

The model accurately predicts ITF between destinations.

Popularity, quality, and distance are key factors influencing ITF.

Explainable AI reveals feature impacts on tourism flow.

Abstract

Tourists often go to multiple tourism destinations in one trip. The volume of tourism flow between tourism destinations, also referred to as ITF (Inter-Destination Tourism Flow) in this paper, is commonly used for tourism management on tasks like the classification of destinations' roles and visitation pattern mining. However, the ITF is hard to get due to the limitation of data collection techniques and privacy issues. It is difficult to understand how the volume of ITF is influenced by features of the multi-attraction system. To address these challenges, we utilized multi-source datasets and proposed a graph-based hybrid deep learning model to predict the ITF. The model makes use of both the explicit features of individual tourism attractions and the implicit features of the interactions between multiple attractions. Experiments on ITF data extracted from crowdsourced tourists' travel notes about the city of Beijing verified the usefulness of the proposed model. Besides, we analyze how different features of tourism attractions influence the volume of ITF with explainable AI techniques. Results show that popularity, quality and distance are the main three influential factors. Other features like coordinates will also exert an influence in different ways. The predicted ITF data can be further used for various downstream tasks in tourism management. The research also deepens the understanding of tourists' visiting choice in a tourism system consisting of multiple attractions.