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The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.

Radical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination.

This study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables.

The results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism.

This study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.

2019冠狀病毒病引致消費者習慣有根本的改變; 這些改變顯示，根據歷史序列而運作的慣常需求預測技巧未必是正確的。這不確性尤以受到大流行極大影響的酒店服務需求為甚。因此，我們擬探討、若把在推特網站上的旅遊活動視為聖雅各之路 (一個重要的朝聖旅遊聖地) 酒店服務需求的預測器，這會否是合適的呢?

本研究比較 SARIMA 時間序列模型與附有外生變數 (SARIMAX)模型兩者在預測旅遊及酒店服務需求方面的表現。為此，研究人員收集在推特網站上發佈的資訊，作為外生變數進行研究。這個樣本涵蓋於2018年1月至2022年9月期間110,456個發佈資訊。

研究結果確認了傳統的時間序列模型，若涵蓋推特網站上的旅遊活動，則其對旅遊需求方面的預測會得到顯著的改善。推特網站的數據，就改善預測實時旅遊需求的準確度，或許可成為有效的工具; 而這發現對旅遊管理會有一定的意義。本研究亦讓我們進一步瞭解朝聖旅遊方面旅客的數碼足跡。

現存文獻甚少探討朝聖旅遊的數字化，而本研究不但在這方面充實了有關的文獻，還使用了一個根據推特網站上使用者原創內容嶄新的方法框架，進行分析和探討。這會幫助酒店從業人員把社交媒體數據轉變為可供酒店管理之用的合宜資訊。

Incorporating dynamic spatial effects exhibits considerable potential in improving the accuracy of forecasting tourism demands. This study aims to propose an innovative deep learning model for capturing dynamic spatial effects.

A novel deep learning model founded on the transformer architecture, called the spatiotemporal transformer network, is presented. This model has three components: the temporal transformer, spatial transformer and spatiotemporal fusion modules. The dynamic temporal dependencies of each attraction are extracted efficiently by the temporal transformer module. The dynamic spatial correlations between attractions are extracted efficiently by the spatial transformer module. The extracted dynamic temporal and spatial features are fused in a learnable manner in the spatiotemporal fusion module. Convolutional operations are implemented to generate the final forecasts.

The results indicate that the proposed model performs better in forecasting accuracy than some popular benchmark models, demonstrating its significant forecasting performance. Incorporating dynamic spatiotemporal features is an effective strategy for improving forecasting. It can provide an important reference to related studies.

The proposed model leverages high-frequency data to achieve accurate predictions at the micro level by incorporating dynamic spatial effects. Destination managers should fully consider the dynamic spatial effects of attractions when planning and marketing to promote tourism resources.

This study incorporates dynamic spatial effects into tourism demand forecasting models by using a transformer neural network. It advances the development of methodologies in related fields.

纳入动态空间效应在提高旅游需求预测的准确性方面具有相当大的潜力。本研究提出了一种捕捉动态空间效应的创新型深度学习模型。

本研究提出了一种基于变压器架构的新型深度学习模型, 称为时空变压器网络。该模型由三个部分组成：时空转换器、空间转换器和时空融合模块。时空转换器模块可有效提取每个景点的动态时间依赖关系。空间转换器模块可有效提取景点之间的动态空间相关性。提取的动态时间和空间特征在时空融合模块中以可学习的方式进行融合。通过卷积运算生成最终预测结果。

结果表明, 与一些流行的基准模型相比, 所提出的模型在预测准确性方面表现更好, 证明了其显著的预测性能。纳入动态时空特征是改进预测的有效策略。它可为相关研究提供重要参考。

所提出的模型利用高频数据, 通过纳入动态空间效应, 在微观层面上实现了准确预测。旅游目的地管理者在规划和营销推广旅游资源时, 应充分考虑景点的动态空间效应。

本研究通过使用变压器神经网络, 将动态空间效应纳入旅游需求预测模型。它推动了相关领域方法论的发展。

La incorporación de efectos espaciales dinámicos ofrece un considerable potencial para mejorar la precisión de la previsión de la demanda turística. Este estudio propone un modelo innovador de aprendizaje profundo para capturar los efectos espaciales dinámicos.

Se presenta un novedoso modelo de aprendizaje profundo basado en la arquitectura transformadora, denominado red de transformador espaciotemporal. Este modelo tiene tres componentes: el transformador temporal, el transformador espacial y los módulos de fusión espaciotemporal. El módulo transformador temporal extrae de manera eficiente las dependencias temporales dinámicas de cada atracción. El módulo transformador espacial extrae eficientemente las correlaciones espaciales dinámicas entre las atracciones. Las características dinámicas temporales y espaciales extraídas se fusionan de manera que se puede aprender en el módulo de fusión espaciotemporal. Se aplican operaciones convolucionales para generar las previsiones finales.

Los resultados indican que el modelo propuesto obtiene mejores resultados en la precisión de las previsiones que algunos modelos de referencia conocidos, lo que demuestra su importante capacidad de previsión. La incorporación de características espaciotemporales dinámicas supone una estrategia eficaz para mejorar las previsiones. Esto puede proporcionar una referencia importante para estudios afines.

El modelo propuesto aprovecha los datos de alta frecuencia para lograr predicciones precisas a nivel micro incorporando efectos espaciales dinámicos. Los gestores de destinos deberían tener plenamente en cuenta los efectos espaciales dinámicos de las atracciones en la planificación y marketing para la promoción de los recursos turísticos.

Este estudio incorpora efectos espaciales dinámicos a los modelos de previsión de la demanda turística mediante el empleo de una red neuronal transformadora. Supone un avance en el desarrollo de metodologías en campos afines.

This study investigates if the causal relationships between the exchange rates and selected inbound markets’ tourism demand are temporary or permanent, and compares market reactions in Türkiye.

Tourism demand is examined with a regional approach, focusing on the geographical markets, namely Europe, Commonwealth of Independent States (CIS) members and Asian countries, as the top inbound tourism markets, in addition to the total number of inbound tourists to Türkiye. Granger, frequency-domain causality, asymmetric Toda–Yamamoto, and asymmetric frequency-domain causality tests were employed to investigate and compare markets on exchange rate–tourism demand relationship for 2008M01-2020M02.

The results indicate that exchange rates affect European tourism demand both in the short and long run. The meaning of this Frequency Domain Causality (FDC) analysis finding shows that the exchange rate has both permanent and temporary effects on European tourists. The relationships are statistically insignificant for CIS members and Asian countries. The exchange rates also permanently affect total inbound tourism demand, but the independent variable has no short-run (temporary) effects on total demand. Asymmetric causality tests confirmed a permanent causality relationship from the positive and negative components of exchange rates to the positive and negative components of European and total tourism demand.

The Granger causality test provides information on the presence of a causal relation, while the FDC test, an extended version of Granger causality, enlightens the short- (temporary) and long-run (permanent) relationships and allows for analyzing the duration of the impact. In addition, asymmetric causality relationships are also investigated in the study. Besides, this study is the first in the literature to examine the relationship between tourism demand and the exchange rate regionally (continentally) for Türkiye.

This study aims to critically evaluate hospitality and tourism demand research and introduce a behavioral economics approach to solve the problems faced by researchers.

Current issues in hospitality and tourism demand analysis are identified through critical reflection, and a behavioral economics approach is adopted to develop a new conceptual framework.

Four issues in hospitality and tourism studies are identified from the microeconomic theory and econometric modeling perspectives. The study’s demand framework provides both a theoretical underpinning and quantitative models to resolve the identified issues. With a focus on consumers’ cost–benefit assessments in light of individual differences and environmental factors, the authors’ conceptual framework represents a new effort to quantify hospitality and tourism demand at the disaggregate level with interactive multiple demand curve estimations.

The study’s analytical framework for hospitality and tourism demand analysis is unique, and it fills the research gap. However, this research is still in the conceptual stage, and the authors leave it to future studies to empirically test the framework.

The proposed demand framework at the disaggregate level will benefit both private and public sectors involved in hospitality and tourism businesses in terms of pricing, marketing and policymaking.

The authors offer a new conceptual model that bridges the gap between aggregate and disaggregate hospitality and tourism demand analyses. Specifically, the authors identify research directions for future hospitality and tourism demand research involving individual tourists/consumers at the disaggregate level.

The study aims to determine the socio-cultural variables that affect Turkey's tourism demand. The study proposes how important socio-cultural determinants as well as economic determinants affect tourism demand.

The study examined a sample of 19 countries sending the most visitors to Turkey between 1996 and 2017 by using panel unit root, panel cointegration tests and cointegration estimator methods. The data set consists of variables such as GDP per capita (lnGDPP), total population number (lnPOP), urbanization level, information and communication technology (lnICT), human development index (lnHDI), education level and death rates (lnDTH).

The findings from the analysis provide evidence that the variables in the models show the expected effects on tourism demand. The findings show that apart from economic variables, socio-cultural variables also have an important effect on tourism demand.

The socio-cultural models used in the study were created using variables that can be quantified. The study results are valid for the countries included in the analysis.

The findings of this study will contribute to policymakers in determining the market for Turkish tourism. The results show that the policies to be prepared by considering the socio-cultural characteristics of countries can increase the tourism demand.

The study is significant in that it focuses on socio-cultural variables rather than economic variables commonly used in the literature. The study is original in terms of both the study sample and the model and considers cross-sectional dependency (CD) and homogeneity.

The purpose of this paper is to review recent studies published from 2007 to 2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field.

Articles on tourism and hotel demand modeling and forecasting published mostly in both science citation index and social sciences citation index journals were identified and analyzed.

This review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, whereas disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior and consumer confidence indicators, among others. More sophisticated techniques such as nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area.

The main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting.

This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices.

The value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

This study aims to examine whether and when real-time updated online search engine data such as the daily Baidu Index can be useful for improving the accuracy of tourism demand nowcasting once monthly official statistical data, including historical visitor arrival data and macroeconomic variables, become available.

This study is the first attempt to use the LASSO-MIDAS model proposed by Marsilli (2014) to field of the tourism demand forecasting to deal with the inconsistency in the frequency of data and the curse problem caused by the high dimensionality of search engine data.

The empirical results in the context of visitor arrivals in Hong Kong show that the application of a combination of daily Baidu Index data and monthly official statistical data produces more accurate nowcasting results when MIDAS-type models are used. The effectiveness of the LASSO-MIDAS model for tourism demand nowcasting indicates that such penalty-based MIDAS model is a useful option when using high-dimensional mixed-frequency data.

This study represents the first attempt to progressively compare whether there are any differences between using daily search engine data, monthly official statistical data and a combination of the aforementioned two types of data with different frequencies to nowcast tourism demand. This study also contributes to the tourism forecasting literature by presenting the first attempt to evaluate the applicability and effectiveness of the LASSO-MIDAS model in tourism demand nowcasting.

The purpose of this paper is to empirically examine the impact of institutional quality on the international tourism demand of India. To carry out the analysis, the study first analyses the impact of composite institutional quality index and then proceeds to examine the impact of each of the individual components of institutional quality on the international tourism demand of India. The impact of income of the tourist originating countries, tourism price, trade openness and Human Development Index (HDI) on tourism demand has also been examined.

The study employed panel autoregressive distributed lag (ARDL) model, with data from top 30 tourist originating countries for India for the period of 1995–2016.

The results indicated that an increase in the income of the tourist originating countries has spillover effects on the development of tourism sector of India. The impact of cost of travel proxied by relative prices between the destination and origin country is found to be negative, however, statistically insignificant. The impact of trade openness and development level of the host country (proxied by HDI) is found to have positive association with the tourism demand. Institutional quality is found to have positive association with international tourism demand of India. Among the individual components of institutional quality, rule of law, regulatory quality, control of corruption and voice and accountability are found to promote the tourism sector development in the economy. Contrarily, the impact of government effectiveness is found to be negative. In the short run, most of the variables were found to support their counterpart results in long run.

This study has practical implication not only in formulating tourism sector policies of the host countries but also for issuing tourist advisories in tourist originating countries. The study holds that policymakers should work for improving institutional environment of the country such as bureaucracy, legislature, regulatory quality, rule of law and for reducing corruption at all levels so as to ensure a sustained rise in tourist inflows to India.

This study validates the link between institutional quality of a country and international demand for its tourism. To the best of the authors' knowledge, the study is the first attempt that has comprehensively analysed the impact of institutional quality on tourism demand in Indian context which has been generally ignored in the tourism literature.

The purpose of this paper is to study determining factors of international tourism demand in Middle Eastern countries.

Panel data pattern is used for data analysis of 1995 to 2013.

Results indicate variables like trade freedom index and gross domestic product (GDP) have positive and significant impact upon tourism demand of the countries of the region. Purchasing power parity (PPP) and GDP per capita are indicators which affect the tourism demand rate in Middle East negatively.

It is estimated that Middle East region will claim for the bulk of tourist arrivals in following years. Therefore, this study is vital for destination managers to plan for demand in future.