The Determinants of the Travel Demand on International Tourist Arrivals to Thailand

The purpose of this study is to develop a travel demand model of international tourist arrivals to Thailand and to assess the impact of crisis incidents on Thailand's tourism industry. A 20-year (1987–2006) annual time series data of “number of international tourist arrivals”, “exchange rate”, “promotion budget”, and dummy variables of “Asia financial crisis”, “special promotional campaigns”, “SARS” and “tsunami” were used to develop the travel demand model by performing a multiple regression analysis. The results showed that travel demand of international tourist arrivals to Thailand could be explained by “exchange rate”, “promotion budget”, “Asia financial crisis” and “SARS”.     

Forecasting tourism demand: Developing a general nesting spatiotemporal model

This study proposes a general nesting spatiotemporal (GNST) model in an effort to improve the accuracy of tourism demand forecasts. The proposed GNST model extends the general nesting spatial (GNS) model into a spatiotemporal form to account for the spatial and temporal effects of endogenous and exogenous variables as well as unobserved factors. As a general specification of spatiotemporal models, the proposed model provides high flexibility in modelling tourism demand. Based on a panel dataset containing quarterly inbound visitor arrivals to 26 European destinations, this empirical study demonstrates that the GNST model outperforms both its non-spatial counterparts and spatiotemporal benchmark models. This finding confirms that spatial and temporal exogenous interaction effects contribute to improved forecasting performance.     

Modeling domestic holiday tourism demand in Australia: Problems and solutions

An econometric model is very useful for understanding the underlying relationship between tourism demand and economic variables such as income and travel prices. However, a long time series horizon of data is essential to run an econometric model that is consistent with economic theory. Although time series data on the number of domestic trips and visitor nights in Australia are available since 1978–79, breaks in the time series in different years have made it difficult to estimate a domestic holiday demand model. It is because the data series in different periods are not directly comparable. In this study, a simple data adjustment technique has been used to obtain comparable data series. Among several econometric demand models, a single equation multivariate time series demand model in a double log linear functional form was found to be the most appropriate and practical model to estimate and analyze the demand parameters of domestic holiday travel in Australia. However, the model with variables in level terms was observed having the “spurious regression problem” which has been corrected using the cointegration and error correction mechanisms. The estimated income and price elasticity of domestic holiday travel demand are consistent with economic theory and therefore can be used for forecasting and other purposes.     

Visiting probability model: a new method for tourist volume forecasting

ABSTRACT

Tourist volume forecasting is an ongoing theme in tourism research. Current methods rely too much on the previous tourist arrivals data. Based on tourism system perspective, we propose a visiting probability model composed of five independent variables: the attractiveness of a destination, the travel time from a origin to the destination, the traffic expense to and from the destination, the physical fatigue travel time and the per capita disposable monthly income of the origin. The model provides a new method for forecasting the number of tourists from a specific origin without historical tourist arrivals data.     

Group pooling for deep tourism demand forecasting

Advances in tourism demand forecasting immensely benefit tourism and other sectors, such as economic and resource management studies. However, even for novel AI-based methodologies, the challenge of limited available data causing model overfitting and high complexity in forecasting models remains a major problem. This study proposes a novel group-pooling-based deep-learning model (GP–DLM) to address these problems and improve model accuracy. Specifically, with our group-pooling method, we advance the tourism forecasting literature with the following findings. First, GP–DLM provides superior accuracy in comparison with benchmark models. Second, we define the novel dynamic time warping (DTW) clustering quantitative approach. Third, we reveal cross-region factors that influence travel demands of the studied regions, including “travel blog,” “best food,” and “Air Asia.”     

中国入境旅游外汇收入季节调整实证分析

旅游业的行业特点决定了中国入境旅游时间序列的发展趋势中存在明显的季节变动因素及不规则因素.本文利用X-12-ARIMA季节调整方法,把中国入境旅游外汇收入序列具体分解为长期趋势因素、季节变动因素及不规则因素,并对各构成因素作了简要分析,以期能对中国入境旅游业的变动规律给出一个相对客观的认识.     

Tourism demand forecasting with online news data mining

This study empirically tests the role of news discourse in forecasting tourist arrivals by examining Hong Kong. It employs structural topic modeling to identify key topics and their meanings related to tourism demand. The impact of the extracted news topics on tourist arrivals is then examined to forecast tourism demand using the seasonal autoregressive integrated moving average with the selected news topic variables method. This study confirms that including news data significantly improves forecasting performance. Our forecasting model using news topics also outperformed the others when the destination was experiencing social unrest at the local level. These findings contribute to tourism demand forecasting research by incorporating discourse analysis and can help tourism destinations address various externalities related to news media.     

Spatial-temporal forecasting of tourism demand

This study conducts spatial-temporal forecasting to predict inbound tourism demand in 29 Chinese provincial regions. Eight models are estimated among a-spatial models (autoregressive integrated moving average [ARIMA] model and unobserved component model [UCM]) and spatial-temporal models (dynamic spatial panel models and space-time autoregressive moving average [STARMA] models with different specifications of spatial weighting matrices). An ex-ante forecasting exercise is conducted with these models to compare their one-/two-step-ahead predictions. The results indicate that spatial-temporal forecasting outperforms the a-spatial counterpart in terms of average forecasting error. Auxiliary regression finds the relative error of spatial-temporal forecasting to be lower in regions characterized by a stronger level of local spatial association. Lastly, theoretical and practical implications are provided.This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecasting, a special selection of research in this field.     

Hawaii tourism to the year 2000 : A Delphi forecast

The Delphi torecasting technique is used to forecast tourism to Hawaii, particularly Oahu, by the year 2000. Local experts and travel agents were questioned on visitor arrivals and percentage of domestic arrivals to Hawaii, market share, visitor-to-resident ratio, maximum visitor accommodation and desirable growth rates, and probable scenarios for Oahu tourism. The results show few significant differences in responses among the groups, and confirmed expectations about convergence and consistency of managerial responses with statistical projections and existing trends. As such, this study demonstrates the value of combining qualitative with quantitative techniques in making long-term forecasts.     

基于人工神经网络的国际入境旅游需求的定量分析与预测--以日本对香港的国际旅游需求分析为例

旅游需求预测研究研究一直是旅游学研究的一个重要课题。本文尝试用人工神经网络模型的的3层BP模型来仿真模拟国际入境旅游需求，并以日本对香港的国际旅游需求为例进行模型验证。其输入层结点为SP、FR、POP、GDE、AH、MK，旅客量为输出节点，得出3层前馈反向传播神经网络模型。最后将模拟结果与目前常用的几种模型利用相同的数据源进行对比，最后发现人工神经网络模型模拟结果与目前常用的几种模型利用相同的数据源进行模拟的结果进行对比，最后发现人工神经网络模型的模拟结果与实际情况最为逼近。     

Modeling Tourism Demand

SUMMARY

Most of the existing studies on tourism demand forecasting apply economic models that use mathematical functions, which require many statistical assumptions and limitations. This paper presents a new approach that applies the rough sets theory to form a forecasting model for tourism demand. The objective of this research is to create patterns which are able to distinguish between the classes of arrivals in terms of volume, based upon differences in the characteristics in each arrival. The information about the arrivals was organized in an Information Table where the number of arrivals corresponds to condition attributes, and the classification was defined by a decision attribute that indicated the forecast categorical value of future arrivals. Utilizing Japanese arrivals data in Hong Kong, empirical results showed the induced decision rules could accurately forecast (86.5%) of the test data.     

Impact of unexpected events on inbound tourism demand modeling: evidence of Middle East Respiratory Syndrome outbreak in South Korea

This paper attempts to evaluate how South Korea’s inbound tourist arrivals from China have been affected by the Middle East Respiratory Syndrome (MERS) outbreak. Using quarterly data, the autoregressive distributed lag model (ADLM) is performed to capture the influence of the MERS outbreak. Estimation results of the general ADLM reveal that the MERS outbreak has a significant adverse impact on the total inbound tourist arrivals from China, as well as on tour arrivals; however, for business, official, and other types of tourist arrivals, its influence is insignificant. Furthermore, the error correction model is estimated to demonstrate the long-run equilibrium and short-run dynamics among the underlying variables. Our analysis not only provides empirical evidence on evaluating the impact of the MERS outbreak on different types of tourism demand, but also identifies main determinants and suggests appropriate model specifications for each type of tourist arrivals.     

基于IOWHA算子的组合预测在中国入境旅游中的应用分析

为提高中国入境旅游人数月度数据序列预测精度,文章选择了目前相对最优单项预测模型——TRAMO/SEATS短记忆预测模型和ARFIMA长记忆预测模型,并根据中国入境旅游人数月度数据序列特点,采用非常适合中国入境旅游人数月度数据序列预测并具有高预测精度的传统线性回归预测模型,然后将各个单项预测模型进行基于IOWHA算子的组合。研究发现：基于IOWHA算子的组合预测模型,达到了目前为止中国入境旅游人数月度数据序列预测的最高精度。最后,根据中国入境旅游人数实际值和组合模型预测值的比较,定量分析世界金融危机等事件对中国入境旅游的影响程度和影响时滞,并探究中国入境旅游未来的发展趋势。     

我国入境旅游人次月度指数预测模型比较研究

需求预测是旅游产业经营决策的基本依据,但产业的广泛关联性与各类突发事件使旅游需求预测尤其是中短期预测较为困难.本文采用X12-ARIMA模型、TRAMO/SEATS模型、ARMA模型与GARCH模型,对异常数据点采用附加的外部冲击调整,利用7种估计方法估计了我国入境旅游人次的月度指数并进行了预测比较,发现采用外部冲击检测的TRAMO/SEATS模型由于能有效提取序列数据的信息,对预测我国入境旅游人次最为有效.     

旅游者中位年龄的几个市场指示意义

年龄是旅游市场细分理论中一个重要的社会—人口学变量,不同的年龄结构对细分市场的旅游行为有着重要的影响。然而,在刻画旅游者年龄结构时,无论是在业界实践还是学界研究中,被普遍采用但标准各异的旅游者年龄"上中下"分组模式却不可避免地制约了相关研究成果之间纵向或横向的比较与验证,需要发展一条可以沟通联系的纽带。文章试图将中位年龄作为这种可能的纽带引入我国旅游市场研究领域,基于国家旅游局公开发布的旅游者年龄统计数据,提出了旅游市场类型的中位年龄划分标准,并在此基础上进一步探讨了旅游者中位年龄在指示市场结构类型、市场环境波动和市场发展分化方面的后效价值,以期为旅游市场营销与管理提供一定的理论依据。     

实现精准目标市场识别的美国入境游客细分

随着游客需求和产品供应的分化,精细化市场细分对目的地的生存发展愈发重要。当前遭遇发展瓶颈的我国入境游就亟须该技术提供科学指导。文章基于Plog心理类型理论,试图识别美国近冒险型消遣游客市场中对访问增长最具杠杆作用的核心人群。细分设计在整体多阶段框架下涵盖了对“前验法”和“后验法”的次序运用。4个属于不同活动组群的近冒险型子细分市场被识别：户外刺激体验者、休闲娱乐追求者、文化探求者和兴趣广泛者“。经济价值组合矩阵”指向文化探求者为未来营销瞄准的最适宜对象。     

An empirical study of inbound tourism demand in China: a copula-GARCH approach

This paper proposes new models for analyzing the volatility and dependence of monthly tourist arrivals to China applying a copula-GARCH approach. A desegregation of the top six origins of China inbound tourists from the period January 1994 to December 2013 is used in this study. The empirical results show that there is a strong seasonal effect in all cases and ?????? some habit persistence on monthly tourist arrival growth rate for South Korea, Russia, the United States (US), and Malaysia. Second, the volatilities of arrival growth rates to China are impacted significantly by their own short- and long-run effects, except for Russia and South Korea. Only short-run shock affects Russian arrivals while only long-run shocks are affecting South Korea arrivals. Third, the conditional dependence among different source countries is found to be positive and significant, but the conditional dependence for all considered pairs is low. Moreover, there is extreme co-movement (tail dependence) between the six major tourism source countries, suggesting the pairwise of international tourist arrivals shows a related increasing or decreasing pattern during extreme events. Implications are discussed and recommendations provided.     

Understanding visitor–resident relations in overtourism: developing resilience for sustainable tourism

While almost all travel destinations seek to increase tourists, less attention is paid to balancing the growth in tourists against consequent visitor–resident irritants, which is essential if the objective is to make tourism more sustainable. Overlooking the carrying capacity of a destination is a common mistake committed when formulating travel visa policies. Overtourism is a term recently used to contextualize this potential hazard to many popular tourist destinations worldwide. One notable case in point is the “multiple-entry permit” policy implemented in Hong Kong which is causing conflicts between mainland Chinese visitors and Hong Kong residents. To investigate the overtourism phenomenon in Hong Kong we develop a hysteresis model. We hypothesized that ceteris paribus, the implementation of a “multiple-entry permit” policy would lead to an overwhelming growth in day-trippers and cause a permanently negative cointegrating relationship with residents’ sentiment. We confirmed our hypothesis by using the bound tests of Autoregressive-Distributed Lag models. Our findings suggest that policymakers should note that the deterioration in visitor–resident relations from overtourism may exhibit a significant hysteresis effect that will persist far beyond the original stimulus. “Developing resilience in tourism” and “exploring sustainable degrowth” are discussed as potential strategies for long-term tourism growth.     

Modelling international monthly arrivals using seasonal univariate long-memory processes

Seasonal long memory has become an alternative credible way when modelling the seasonal component in many time series. In this paper, a procedure is employed that permits consideration of unit and fractional orders of integration at seasonal frequencies in a raw time series. This method is applied to the international monthly arrivals in the US, using both aggregated and disaggregated data. The results show that the total number of arrivals in the US can be specified in terms of a seasonal I(d) model with d higher than zero but smaller than one, implying long memory and mean reverting behaviour. Attempting to summarize the conclusions for the individual locations, leaves the impression that Asia, Central and South America, Eastern Europe and Oceania present the smallest degrees of integration. On the other hand, Africa and the Caribbean provide the highest values, implying for these two locations a strong degree of persistence to the US as a destination. Finally, another application, based on a longer dataset, is also employed to examine the forecasting properties of this type of model.     

上海市入境旅游市场影响因素分析与预测——以德国、法国、英国、美国与泰国客源国为例

入境旅游是衡量一个国家知名度、影响力和旅游发展水平的主要因素,也是赚取外汇和旅游收入的重要路径。上海是外国游客入境重要的目的地,也是中国最大的入境游客中转站。文章分析了德国、法国、英国、美国、泰国五个上海主要入境旅游客源国2004年第一季度至2018年第三季度的数据,运用计量经济学方法建模,并实证分析了上海入境旅游需求的影响因素。研究表明,口碑效应、客源地的收入水平与上海入境旅游需求正相关;上海入境旅游具有较大的季节波动特点,冬夏两季入境游客数量减少;世博会对上海入境旅游拉动作用较大。同时,对德国、法国、泰国三大市场未来十年的旅游季度需求进行了预测,预测发现,德国、法国、泰国三大市场未来十年都有较大增长,特别是泰国市场的年均增长率达到4%。