Predict Tourism Demand Research Articles

Reference-dependence in multi-destination choice: A heterogeneous reference point perspective

Destination choice in multi-destination trips is crucial for understanding tourists' preferences, predicting tourism demand and flow, and fostering collaborative opportunities between destinations. This study utilises prospect theory to examine how tourists select a stop within a multi-destination trip and to explore the role of other chosen stops' attributes as a reference point when tourists plan their itinerary. The results reveal dynamic perceptions of gains and losses relative to reference points that vary across individual characteristics and destination attributes. Additionally, heterogeneity is revealed in tourists' reference-dependent patterns, which can be categorised as immediate- and delayed-gratification reference dependencies. These findings contribute to the destination choice discourse by proposing new directions for comprehending tourists' heterogeneous reference dependencies and attitudes in sequential destination choices.

Network Big Data Analysis with Multi-Factor Hashing Ethereum Classification (Mfh-Ec): An Approach to Develop Strategy of the Tourism Industry Under the Post-Epidemic Situation

In the tourism industry, big data has emerged as a revolutionizing the way businesses operate and travelers experience destinations. With the vast amount of data generated from online bookings, social media interactions, and mobile applications, tourism companies can gain valuable insights into traveler preferences, behavior patterns, and market trends. This paper proposes a development strategy for the tourism industry in the post-epidemic situation, leveraging network big data analysis with Multi-Factor Hashing Ethereum Classification (MFH-EC). By harnessing the power of network big data, this strategy aims to provide insights into changing traveler preferences, market dynamics, and risk factors in the wake of the pandemic. The MFH-EC model facilitates the classification and analysis of diverse factors influencing tourism development, including economic indicators, health and safety measures, environmental conditions, and traveler sentiment. Through simulated experiments and empirical validations, the effectiveness of the proposed strategy is assessed, demonstrating significant improvements in predictive accuracy and decision-making capabilities. For instance, the MFH-EC model achieved an 80% accuracy rate in predicting tourism demand shifts and a 30% reduction in forecasting errors compared to traditional methods. These results underscore the potential of network big data analysis with MFH-EC in guiding strategic decision-making and fostering sustainable recovery and growth in the tourism industry post-epidemic.

Google Trends data and transfer function models to predict tourism demand in Italy

PurposeThe tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty and in which some megatrends have the potential to reshape society in the next decades. This paper, considering the opportunity offered by the application of the quantitative analysis on internet new data sources, proposes a prediction method using Google Trends data based on an estimated transfer function model.Design/methodology/approachThe paper uses the time-series methods to model and predict Google Trends data. A transfer function model is used to transform the prediction of Google Trends data into predictions of tourist arrivals. It predicts the United States tourism demand in Italy.FindingsThe results highlight the potential expressed by the use of big data-driven foresight approach. Applying a transfer function model on internet search data, timely forecasts of tourism flows are obtained. The two scenarios emerged can be used in tourism stakeholders’ decision-making process. In a future perspective, the methodological path could be applied to other tourism origin markets, to other internet search engine or other socioeconomic and environmental contexts.Originality/valueThe study raises awareness of foresight literacy in the tourism sector. Secondly, it complements the research on tourism demand forecasting by evaluating the performance of quantitative forecasting techniques on new data sources. Thirdly, it is the first paper that makes the United States arrival predictions in Italy. Finally, the findings provide immediate valuable information to tourism stakeholders that could be used to make decisions.

Cross-domain dynamics: How geopolitical and climate policy uncertainties shape tourism patterns in the United States

Predicting tourism demand is crucial for policymakers and stakeholders in the tourism industry, enabling them to enhance promotional strategies, optimize marketing efforts, align investments, and achieve operational efficiency. While existing research has highlighted the negative impact of global risk factors such as geopolitical risks, economic policy uncertainty, and climate changes on tourism demand, the specific influence of climate policy changes and their differential impact on tourist arrivals has been less explored. Addressing this gap, our study employs bivariate, multiple, and partial wavelet methods to analyze the differential impact of climate policy uncertainty and geopolitical risk on tourism arrivals in the United States. Utilizing recent monthly data from January 1996 to August 2022, we uncover significant time-frequency varying dependencies between these factors and tourism demand. Our causality analysis reveals that both geopolitical risk and climate policy uncertainty are key determinants of tourism demand in the U.S. Furthermore, the Nonlinear Autoregressive Distributed Lag model corroborates the findings of the wavelet analysis, highlighting asymmetrical interactions in the long run. These findings offer valuable insights and have significant implications for policymakers and industry stakeholders, suggesting the need for adaptive and responsive tourism strategies that consider the evolving landscape of geopolitical and climate policy uncertainties.

Predicting Tourism Demand by Combining Search Engine Data

Predicting Tourism Demand by Combining Search Engine Data

Research on Modeling of Influencing Factors of New Eco-tourism Management Based on Economic Fluctuation Prediction

This paper begins by analyzing the effect of market economic fluctuations on the digital economy's tourism economy. Second, this paper uses a city cluster in the Yangtze River Delta in China as an example, selects the tourism economic data of the region from 2011 to 2021, and forecasts the tourism development potential of the region from 2022 to 2026 using the conventional methods for predicting economic fluctuations. However, conventional models for predicting economic fluctuations, such as ARIMA, have large prediction errors. This paper combines the RBF neural network and exponential smoothing method and compares them to GM(1, 1), ARIMA, and other models. The findings indicate that the tourism economy in this region is spatially distinct and relatively stable. Therefore, the methodology presented in this paper is capable of accurately predicting tourism demand and growth potential.

Tourism Forecasting of “Unpredictable” Future Shocks: A Literature Review by the PRISMA Model

This study delves into the intricate process of predicting tourism demand, explicitly focusing on econometric and quantitative time series analysis. A meticulous review of the existing literature is carried out to comprehensively understand the various methods for forecasting “unpredictable” shocks of tourism demand on an ex-ante basis. The PRISMA method has been implemented. Drawing on scholarly research, this study pinpoints the critical challenges in accurately predicting tourism demand, making it a valuable resource for tourism professionals and researchers seeking to stay on top of the latest forecasting techniques. Moreover, the study includes an overview of published manuscripts from the current decade, with mixed results from the 32 manuscripts reviewed. The study concludes that virtual tourism, augmented reality, virtual reality, big data, and artificial intelligence all have the potential to enhance demand forecasting in time series econometrics.

Tourism demand forecasting based on user-generated images on OTA platforms

ABSTRACT Tourists’ images of destinations posted on online travel agency (OTA) platforms are important information sources for potential tourists to perceive and construct destination images. These perceptions can then inform travel decisions. We investigated the roles of the aesthetics of user-generated images on OTA platforms in tourism demand forecasting. Specifically, the aesthetics of images of three popular scenic spots in Hong Kong were used to predict tourism demand from the region’s two largest short-haul markets and largest long-haul market using a seasonal autoregressive moving average with exogenous factors (SARIMAX) model. Seasonal naïve, SARIMA, and SARIMAX models involving search query data were taken as benchmarks. Results showed that (1) image aesthetics could help make more accurate tourism demand forecasting; and (2) as an additional variable, image aesthetics could supplement search query-based volume variables to enhance tourism demand forecasting.

The impact of tree-based machine learning models, length of training data, and quarantine search query on tourist arrival prediction’s accuracy under COVID-19 in Indonesia

ABSTRACT This study presents the extreme gradient boosting (XGBoost) and random forest (RF) models to predict tourism demand by incorporating international COVID-19 cases, international tourist arrivals, and the destination's quarantine policy predictors. Unlike other ‘black box’ machine learning models, those two tree-based models offer better interpretability with explicit feature importance and tree structure representations. This paper evaluates the accuracy of these models in predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using long-range (January 2008–June 2021) and short-range (January 2018–June 2021) training datasets. The performance of these two models is compared with benchmark models, such as the artificial neural network, autoregressive integrated moving average, and seasonal ARIMA models. In general, the tree-based machine learning models outperformed all benchmark models. International COVID-19 cases and tourist arrivals predictors have dominating feature importance scores in XGBoost models. Meanwhile, Google trends keywords on quarantine policies show significant importance in RF models but not in the XGBoost models. Moreover, RF models are better than the XGBoost models in terms of accuracy and overcoming overfitting cases.

Forecasting and modelling for the inbound tourism demand volatility

Many past studies conducted have explored and focused primarily on forecasting and predicting tourism demand. However, this research aims to concentrate on the aspect of how tourism demand volatility can be forecasted and accounted for, rather than solely on how we can forecast and anticipate tourism demand itself. Seven source countries for monthly tourist arrivals in Malaysia will be considered and focused as a case study in this paper. Furthermore, the model theory of generalised autoregressive conditional heteroscedastic (GARCH) family volatility are explored in its relevance and introduced in this study. Additionally, the performance and evaluation of a new adaptive forecasting method, smooth transition exponential smoothing (STES), will be compared to GARCH-type models and several ad hoc methods for Malaysia's tourism demand volatility forecasting and modelling. Notably, our research conducted reported findings that monthly seasonality impacts existed in the mean equation, and the GARCH family models revealed that news shock continuously impact on Malaysia's tourism demand volatility. The EGARCH and GJR models utilised in this study also exhibited the asymmetric and leverage effects. Interestingly, it was also observed that the STES model surpassed GARCH family models and several ad hoc methods in forecasting and modelling.

An Effective Model for Forecasting Travel Consumer Demand Using Big Data Analysis

As a result of gathering information from multiple consumer centers, big data (BD) assists in analyzing traveler patterns and developing a unique marketing plan tailored to the target demographic. BD tourism forecasting is a relatively new academic field because of the challenges in capturing, gathering, and modeling this sort of data due to its inherent privacy and economic importance. The growth rate of cruise tourists has slowed down after years of rapid expansion. Investing in homeports, cruise ships, and promotional activities carries a growing danger of financial loss. To make investment decisions and prepare for the future, it is necessary to predict tourism demand. We present the least-squares vector regression (LSVR) model with the gravitational search method for forecasting demand for cruise tourism (FCT) based on BD to improve forecasting performance. As a part of the proposed model forecasting demand for cruise tourism based on big data (FDCT-BD), hyper-parameters of the LSVR model are improved using an algorithm and by comparing these models with various configuration combinations. This paper forecasts tourist arrivals based on internet BD from a search engine and online review platforms and the comparative advantage of multi-platform forecasting over single-platform forecasting based on online review data. However, the results show that the methodology’s recommended framework is successful and that BD may estimate cruise tourist demand with enhanced performance and accuracy 93.8% and 97.9%, respectively.

Tourism Demand Modelling and Forecasting: A Review of Literature

This research examines 145 key papers from 1979 to 2020 in order to gain a better sense of how tourism demand forecasting techniques have changed over time. The three types of forecasting models are econometric, time series, and artificial intelligence (AI) models. Econometric and time series models that were already popular in 2005 maintained their popularity, and were increasingly used as benchmark models for forecasting performance assessment and comparison with new models. In the last decade, AI models have advanced at an incredible rate, with hybrid AI models emerging as a new trend. In addition, some new developments in the three categories of models, such as mixed frequency, spatial regression, and combination and hybrid models have been introduced. The main conclusions drawn from historical comparisons forecasting methods are that forecasting models have become more diverse, that these models have been merged, and that forecasting accuracy has improved. Given the complexities of predicting tourism demand, there is no single approach that works well in all circumstances, and forecasting techniques are still evolving.

ESTIMATING THE OCCUPANCY RATE OF AN ACCOMMODATION BUSINESS USING ARTIFICIAL NEURAL NETWORKS

Tourism is one of the sectors that is highly influential in countries' economy. Tourism activity can be realized for many reasons, especially for business or cultural purposes. Such as Turkey, for countries where tourism is intensively carried out, It is very important to predict tourism demand. Many reasons such as determining the workforce to be employed in tourism and the accommodation infrastructure needed have increased in recent years the studies to determine the tourism demands in advance. In this study, the demand for tourism is handled in terms of accommodation establishments. It is aimed to estimate the occupancy rate for a accommodation business in Sakarya. Several factors that have an effect on the occupancy rate have been detected and it has been ensured that the occupancy rate can be predicted with a high estimation rate by using artificial neural networks which are one of the artificial intelligence techniques.

Tourism Demand Forecasting: A Decomposed Deep Learning Approach

Tourism planners rely on accurate demand forecasting. However, despite numerous advancements, crucial methodological issues remain unaddressed. This study aims to further improve the modeling accuracy and advance the artificial intelligence (AI)-based tourism demand forecasting methods. Deep learning models that predict tourism demand are often highly complex and encounter overfitting, which is mainly caused by two underlying problems: (1) access to limited data volumes and (2) additional explanatory variable requirement. To address these issues, we use a decomposition method that achieves high accuracy in short- and long-term AI-based forecasting models. The proposed method effectively decomposes the data and increases accuracy without additional data requirement. In conclusion, this study alleviates the overfitting issue and provides a methodological contribution by proposing a highly accurate deep learning method for AI-based tourism demand modeling.

MULTIPLICIDADE E COMPLEXIDADE DOS MOTIVOS DE VIAGEM:

Este artigo examina os motivos de viagem, buscando discutir e esclarecer a natureza dessa variável. Asformas tradicionais de coleta e apresentação desses motivos são inadequadas à medida quesimplificam a realidade, desconsiderando aspectos relevantes para uma análise abrangente do tema.Uma primeira omissão é relativa à existência de múltiplos motivos para uma única viagem, já que amaioria dos levantamentos atualmente registra apenas o motivo principal da viagem. Outra questão é aheterogeneidade abrangida pelas categorias amplas geralmente utilizadas para descrever os motivos deviagem. Este artigo apresenta e discute o resultado de quatro pesquisas que entrevistaram um total de4216 turistas que visitaram a cidade de São Paulo. A multiplicidade e a heterogeneidade dos motivosde viagem são examinadas em detalhe a partir da perspectiva dos turistas. São apresentadasrecomendações para melhor especificar os conceitos usuais de motivo da viagem. Este trabalhotambém sugere que os questionários de pesquisa de demanda turística deveriam ser mais detalhadospara capturar os motivos de viagem de forma adequada. A multiplicidade e a complexidade dosmotivos de viagem não devem ser subestimadas. Reconhecer os múltiplos motivos e aheterogeneidade abarcada pelas categorias usuais de motivos são passos importantes para a melhorcompreensão, explicação e previsão da demanda e do consumo turístico.

An Inter-Comparison of the Holiday Climate Index (HCI:Beach) and the Tourism Climate Index (TCI) to Explain Canadian Tourism Arrivals to the Caribbean

Through an empirical investigation of the historical relationship between the destination climate and tourist arrivals in the Caribbean, this study presents the first revealed preference evaluation of a climate index informed by tourists’ stated climatic preferences for coastal-beach tourism (i.e., a sun-sand-surf or 3S travel market). The goal of this multi-organization collaboration was to examine the potential application of a newly designed climate index—the Holiday Climate Index (HCI):Beach—for three Caribbean destinations (Antigua and Barbuda, Barbados, Saint Lucia). This paper provides an overview of the evolution of climate indices, including the development of the (HCI):Beach. To test the validity of climate indices for a beach travel market, daily climate ratings based on outputs from the Tourism Climate Index and the HCI were correlated with monthly arrivals data from Canada (a key source market) at an island destination scale. The results underscore the strength of the new index, with each destination scoring consistently higher using the HCI:Beach, including a stronger relationship (R2) between index scores and tourist arrivals. These findings demonstrate the value of combining stated and revealed preference methodologies to predict tourism demand and highlight opportunities for future research.

Forecasting tourism demand with KPCA-based web search indexes

Search query data (SQD) can be helpful in predicting tourism demand by generating web search indexes. However, valuable nonlinear information in SQD may be neglected by researchers. To effectively capture the nonlinear information, we used kernel principal component analysis (KPCA) to extract web search indexes from SQD. Then, several models with KPCA-based web search indexes were developed for tourism demand forecasting. An empirical study was conducted with collected SQD and real data of tourist arrivals at Hong Kong. The results suggest that models with KPCA-based web search indexes are more accurate than other models because of the nonlinear data processing ability of the KPCA and demonstrate that KPCA-based web search indexes can be excellent predictors for tourism demand forecasting.

Conceptualizing vacation dedication

ABSTRACT To better understand and predict tourism demand, we introduce the concept of vacation dedication to the tourism literature. Vacation dedication is a form of enthusiasm for and devotion to going on vacation. It is an enduring urge to go on a vacation that is so strong that it persists in spite of external constraints. We empirically test its links to the well-established constructs of travel involvement, commitment, and tourist identity. Affective commitment emerges as most strongly associated with vacation dedication, pointing to an important emotional component.

Data source combination for tourism demand forecasting

Search engine data are of considerable interest to researchers for their utility in predicting human behaviour. Recently, search engine data have also been used to predict tourism demand (TD). Models developed based on such data generate more accurate forecasts of TD than pure time-series models. The aim of this article is to examine whether combining causal variables with search engine data can further improve the forecasting performance of search engine data models. Based on an artificial neural network framework, 168 observations during 2005–2018 for short-haul travel from Hong Kong to Macau are involved in the test, and the empirical results suggest that search engine data models with causal variables outperform models without causal variables and other benchmark models.

Fine-grained tourism prediction: Impact of social and environmental features

Accurate predictions about future events is essential in many areas, one of them being the Tourism Industry. Usually, cities and countries invest a huge amount of money for planning and preparation in order to welcome (and profit from) tourists. The success of many businesses depends largely or totally on the state of tourism demand. Estimation of tourism demand can be helpful to business planners in reducing the risk of decisions regarding the future since tourism products are, generally speaking, perishable (gone if not used). Prior studies in this domain focus on forecasting for a whole country and not for fine-grained areas within a country (e.g., specific touristic attractions) mainly because of lack of data. Our article tackles exactly this issue. With the rapid popularity growth of social media applications, each year more people interact within online resources to plan and comment on their trips. Motivated by such observation, we here suggest that accessible data in online social networks or travel websites, in addition to environmental data, can be used to support the inference of visitation count for either indoor or outdoor touristic attractions. To test our hypothesis we analyze visitation counts, environmental features and social media data related to 27 museums and galleries in U.K as well as 76 national parks in the U.S. Our experimental results reveal high accuracy levels (above 92%) for predicting tourism demand using features from both social media and environmental data. We also show that, for outdoor attractions, environmental features have better predictive power while the opposite occurs for indoor attractions. In any case, best results, in all scenarios, are obtained when using both types of features jointly. Finally, we perform a detailed failure analysis to inspect the cases in which the prediction results are not satisfactory.