A Study On The Influencing Factors Of Tourism Demand From Mainland China To Hong Kong

Liu, Han; Liu, Wei; Yong-lian, Wang

doi:10.1177/1096348020944435

Cited by 20 publications

(11 citation statements)

References 61 publications

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“…Results suggested a recovery of tourism arrivals of 10% to 70% compared to 2019 under different scenarios. Liu and colleagues (2021) developed a scenario-based judgmental forecast technology based on a novel index and found that the extent of recovery depended on the destination’s dependence on long-haul markets. Kourentzes and colleagues (2021) combined multiple forecasting methods to accomplish the tourism forecasting task in the first stage and conducted judgmental adjustment of model-based forecasting in the second stage.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, with the development of the Internet and other information sources, tourists tend to collect information about a destination in advance, such as hotels, weather, and transportation. This behavior has triggered the rise and development of search engine data ( Liu, Liu et al, 2021 ). Owing to their real-time and high-frequency characteristics, search engine data are used in tourism demand forecasting to supplement traditional data sources and obtain a better grasp of the future trends in tourism ( Li & Law, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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Tourism Demand Interval Forecasting Amid COVID-19: A Hybrid Model With a Modified Multi-Objective Optimization Algorithm

Wang

Zhang

Liu

et al. 2023

Journal of Hospitality & Tourism Research

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A hybrid tourism demand interval forecasting system is proposed consisting of two parts: the construction of forecasting interval based on lower and upper bound estimates, and the forecasting interval adjustment based on an optimized reduction coefficient. Coronavirus factors are added as input variables to improve forecasting performance. A new multi-objective optimization algorithm is proposed to construct a feature selection method, optimize the forecasting model, and estimate the optimal reduction coefficient. The results of the experiments show that the proposed system has a powerful interval forecasting ability, which provides crucial guidance for balancing the recovery of the tourism industry and the control of the epidemic spread during the COVID-19 pandemic, and contributes to contingency planning for tourism practitioners and managers.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tourism Demand Interval Forecasting Amid COVID-19: A Hybrid Model With a Modified Multi-Objective Optimization Algorithm

Wang

Zhang

Liu

et al. 2023

Journal of Hospitality & Tourism Research

View full text Add to dashboard Cite

show abstract

“…Wickramasinghe and Ratnasiri [ 5 ] used travel data classified by region and time and Google search data to improve the accuracy of travel forecasts in Sri Lanka during the COVID-19 pandemic. Liu et al [ 40 ] and Zhang et al [ 13 ] proposed scenario-based judgmental forecasting to analyze the amount of tourism recovery after the COVID-19 epidemic, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

Interpretable tourism volume forecasting with multivariate time series under the impact of COVID-19

Wang

Tao

et al. 2022

Neural Comput & Applic

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This study proposes a novel interpretable framework to forecast the daily tourism volume of Jiuzhaigou Valley, Huangshan Mountain, and Siguniang Mountain in China under the impact of COVID-19 by using multivariate time-series data, particularly historical tourism volume data, COVID-19 data, the Baidu index, and weather data. For the first time, epidemic-related search engine data is introduced for tourism demand forecasting. A new method named the composition leading search index–variational mode decomposition is proposed to process search engine data. Meanwhile, to overcome the problem of insufficient interpretability of existing tourism demand forecasting, a new model of DE-TFT interpretable tourism demand forecasting is proposed in this study, in which the hyperparameters of temporal fusion transformers (TFT) are optimized intelligently and efficiently based on the differential evolution algorithm. TFT is an attention-based deep learning model that combines high-performance forecasting with interpretable analysis of temporal dynamics, displaying excellent performance in forecasting research. The TFT model produces an interpretable tourism demand forecast output, including the importance ranking of different input variables and attention analysis at different time steps. Besides, the validity of the proposed forecasting framework is verified based on three cases. Interpretable experimental results show that the epidemic-related search engine data can well reflect the concerns of tourists about tourism during the COVID-19 epidemic.

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“…Considering that the transportation system of most provincial tourism destinations includes both HSR and aviation, the examination of the combined effect of those two transportation infrastructures on tourism development is necessary and urgent. As a result of the COVID-19 pandemic, demand for international travel has dropped significantly [31]. Thus, domestic tourism demand has become an indispensable force in stimulating economic Land 2023, 12, 216 2 of 18 growth [32].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Combined Effects of Transportation Infrastructure on Regional Tourism Development in China Using a Spatial Econometric Model (GWPR)

Shen

Yin

Niu

2023

Land

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Transportation infrastructure plays an important role in tourism, and the spatial econometric model (GWPR) can offer quantitative support for regionalized development policies in transportation infrastructure. Panel data from 30 provinces were collected for a decade before the COVID-19 pandemic. We show that the GWPR model is a superior tool for assessing the combined impact of transportation infrastructure on tourism and its spatial heterogeneity. The effects of transportation infrastructure on tourism have historically been overwhelmingly positive, with the positive effect of high-speed rail expanding over the decade, while the positive effect of air travel contracted. The combined effects of transportation infrastructure vary across space and time. Additionally, the evolution of the effects exhibits spatial heterogeneity. The 30 provinces in this study are categorized into five types, and targeted implementation strategies for transportation infrastructure are formulated.

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A Study On The Influencing Factors Of Tourism Demand From Mainland China To Hong Kong

Cited by 20 publications

References 61 publications

Tourism Demand Interval Forecasting Amid COVID-19: A Hybrid Model With a Modified Multi-Objective Optimization Algorithm

Tourism Demand Interval Forecasting Amid COVID-19: A Hybrid Model With a Modified Multi-Objective Optimization Algorithm

Interpretable tourism volume forecasting with multivariate time series under the impact of COVID-19

Assessing the Combined Effects of Transportation Infrastructure on Regional Tourism Development in China Using a Spatial Econometric Model (GWPR)

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