Tourism demand forecasting from the perspective of mobility

A brand-new predictive variable generated from intercity population mobility big data

Datos Bibliográficos

ID12462004
AutoresYong Li (0000-0002-4884-9930, Capital University of Economics and Business), Yunpeng Li (0009-0003-6213-9905, Capital University of Economics and Business), Jingjing Li (0009-0006-0033-1522, Capital University of Economics and Business), Shihan Ma (0000-0003-3293-6615, Zhejiang University), Ping Gao (0000-0002-9683-1989, Chinese Academy of Sciences)
Año2022
Volumen27
Número5
Páginas526-546
Fecha de publicación2022-05-04
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaAsia Pacific Journal of Tourism Research (JOURNAL)
Identificadores de la revistaISSN: 1094-1665 • E-ISSN: 1741-6507
EditorialRoutledge (PUBLISHER • GB)
DOI10.1080/10941665.2022.2091941
OpenAlexW4293246285
IdiomaEN
Citas recibidas12
Referencias citadas70

Few studies have investigated tourism demand forecasting from a mobility perspective. We explore whether population mobility data improve the ability to forecast tourist volumes in scenic spots. We selected Jiuzhaigou and Mount Siguniang in China as study cases and ensured the robustness of the results using single- and multistep rolling evaluation techniques. The findings reveal a significant correlation, long-term cointegration, and two-way Granger causality between intercity population flow and tourist flow. The comparison model with population flow as a variable outperforms the benchmark model. We prove the feasibility of leveraging population flow in tourism demand forecasting and suggest further research

Cointegration · Econometrics · Economic geography · Economics · Geographic mobility · Geography · Granger causality · Population · Robustness (evolution · Tourism · Variable (mathematics · Computer Science · Diverse Aspects of Tourism Research · Human Mobility and Location-Based Analysis · Mathematics · Urban Transport and Accessibility