Big Data-driven Tourist Flow focasting with Interpretable Machine Learning

• 영문명: Big Data-driven Tourist Flow focasting with Interpretable Machine Learning
• 발행기관: 한국문화관광학회
• 저자명: Xin Li Yisuan Wang Shouyang Wang
• 간행물 정보: 『문화관광연구』제25권 제1호, 27~47쪽, 전체 21쪽
• 주제분류: 사회과학 > 관광학
• 파일형태: PDF
• 발행일자: 2024.06.30

국문 초록

Although user-generated content (UGC) in the digital economy has garnered substantial attention for economic forecasting, the impact of marketer-generated content (MGC) on tourism demand has been empirically established but less explored in combination with UGC. Utilizing daily visitor data from the Palace Museum in China as a case study, this research examines this understudied relationship and its role in forecasting visitor numbers. We formulate and evaluate six specific machine-learning models using big data collected from two prominent social media platforms: TikTok and Weibo. To enhance model interpretability, we introduce a novel methodology grounded in Shapley regression and weighted contribution coefficients, providing a quantitative assessment of the individual impacts of MGC and UGC. Empirical results reveal that MGC serves as a leading predictor for daily visitor arrivals at the Palace Museum, and optimal accuracy is achieved when both MGC and UGC are integrated into the predictive algorithm. This study fills an existing gap in the literature and offers both theoretical and practical contributions to tourism demand forecasting.

목차

Introduction

Interpretable Forecasting Framework and Methods

Empirical Research

Literature review

Methods

Data Collection

Data Analysis

Findings

Gratification at pre-travel stage

Gratification at Post-Travel Stage

Gratification at Post-Travel Stage

Discussion and Conclusion

Limitations and Future Research Directions

Reference

키워드

big data machine learning interpretability tourist flow forecasting internet- generated content