Tourism demand forecasting: A deep learning approach

Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. Using a deep learning approach, this research studied the framework in forecasting monthly Macau tourist arrival volumes. The empirical results demonstrated that the deep learning approach significantly outperforms support vector regression and artificial neural network models. Moreover, the construction and identification of highly relevant features from the proposed deep network architecture provide practitioners with a means of understanding the relationships between various tourist demand forecasting factors and tourist arrival volumes.This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecasting, a special selection of research in this field     

Tourism demand forecasting with time series imaging: A deep learning model

To leverage computer vision technology to improve the accuracy of tourism demand forecasting, a model based on deep learning with time series imaging is proposed. The model consists of three parts: sequence image generation, image feature extraction, and model training. In the first part, the tourism demand data are encoded into images. In the second part, the convolution and pooling layers are used to extract features from the obtained images. In the final part, the extracted features are input into long short-term memory networks. Based on historical tourism demand data, the model for forecasting future tourism demand can be obtained. The performance of the proposed model is experimentally assessed through comparing against seven benchmark models.     

A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting

This study reviews 211 key papers published between 1968 and 2018, for a better understanding of how the methods of tourism demand forecasting have evolved over time. The key findings, drawn from comparisons of method-performance profiles over time, are that forecasting models have grown more diversified, that these models have been combined, and that the accuracy of forecasting has been improved. Given the complexity of determining tourism demand, there is no single method that performs well for all situations, and the evolution of forecasting methods is still ongoing.This article also launches the Annals of Tourism Research Curated Collection on tourism demand forecasting, which contains past and hot off the press work on the topic and will continue to grow as new articles on the topic appear in Annals.     

Customer purchase forecasting for online tourism: A data-driven method with multiplex behavior data

Online tourism has received increasing attention from scholars and practitioners due to its growing contribution to the economy. While related issues have been studied, research on forecasting customer purchases and the influence of forecasting variables, online tourism is still in its infancy. Therefore, this paper aims to develop a data-driven method to achieve two objectives: (1) provide an accurate purchase forecasting model for online tourism and (2) analyze the influence of behavior variables as predictors of online tourism purchases. Based on the real-world multiplex behavior data, the proposed method can predict online tourism purchases accurately by machine learning algorithms. As for the practical implications, the influence of behavior variables is ranked according to the predictive marginal value, and how these important variables affect the final purchase is discussed with the help of partial dependence plots. This research contributes to the purchase forecasting literature and has significant practical implications.     

Tourism demand forecasting models: Choice of appropriate variable to represent tourists' cost of living

Price is generally regarded as a major determinant of demand. Tourism has two price elements - the cost of travel to the destination and the cost of living for tourists in the destination. Previous studies where econometric forecasting models have been developed for international tourism demand usually take the consumer price index in a country to be a proxy for the cost of tourism in that country owing to lack of appropriate data. This article attempts to evaluate the performance of proxy variables compared with a specific tourists' cost of living variable within the context of tourism demand forecasting models.     

Density tourism demand forecasting revisited

This study used scoring rules to evaluate density forecasts generated by different time-series models. Based on quarterly tourist arrivals to Hong Kong from ten source markets, the empirical results suggest that density forecasts perform better than point forecasts. The seasonal autoregressive integrated moving average (SARIMA) model was found to perform best among the competing models. The innovation state space models for exponential smoothing and the structural time-series models were significantly outperformed by the SARIMA model. Bootstrapping improved the density forecasts, but only over short time horizons.This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecasting, a special selection of research in this field.     

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.     

Tourism and online photography

This paper documents the use of online travel photo-sharing technologies among Hong Kong residents. The advent of Web 2.0 is changing fundamentally the tourist information search and destination choice process. Yet, to date, no study has documented Web 2.0 usage levels and preferences for certain media for posting travel photographs across an entire community. This study reveals that some 89% of pleasure travelers take photographs and that 41% of them posted their photographs online. Social network sites (SNSs), instant messaging, online photo albums and personal blogs were the most popular media used. In general, people who post photographs online tend to be younger, better educated, and earn a higher income than those who do not. The study determined that most people also use multiple media platforms to disseminate their images. Cluster analysis based on the four major styles of online platform identified five cohorts of users who differed in number and type of media used, demographic profile and travel motive. Implications for destination promotion are discussed.     

Multi-attraction,hourly tourism demand forecasting

Forecasting tourism demand for multiple tourist attractions on an hourly basis provides important insights for effective and efficient management, such as staffing and resource optimization. However, existing forecasting models are not well equipped to hand the hourly data, which is dynamic and nonlinear. This study develops an improved, artificial intelligent-based model, known as Correlated Time Series oriented Long Short-Term Memory with Attention Mechanism, to solve this problem. The validity of the model is verified through a forecasting exercise for 77 attractions in Beijing, China. The results show that our model significantly outperforms the baseline models. The study advances the tourism demand forecasting literature and offers practical implications for resource optimization while enhancing staff and customer satisfaction.     

Tourism jobs in demand: Where the best contracts and high salaries go at online offers

It has been suggested that the tourism sector is one in which temporary and low-skilled jobs abound. The aim of this study is to analyze the educational and experience requirements associated with job offers in tourism and their corresponding contracts and salaries. Nine thousand job advertisements were analyzed from one of the biggest employment websites in Spain. Online job offers allow a profile of employers' preferences to be drawn up in terms of occupations, educational levels and required experience, as well as the terms of contracts and salaries offered. Internal heterogeneity in the tourism sector arising from the coexistence of different functions and skills is highlighted. Our analysis shows that the occupation crucially conditions salary differences and contract types in tourism job offers.     

Bayesian BILSTM approach for tourism demand forecasting

The tourism sector, with its perishable nature of products, requires precise estimation of demand. To this effect, we propose a deep learning methodology, namely Bayesian Bidirectional Long Short-Term Memory (BBiLSTM) network. BiLSTM is a deep learning model, and Bayesian optimization is utilized to optimize the hyperparameters of this model. Five experiments using the tourism demand data of Singapore are conducted to ascertain the validity and benchmark the proposed BBiLSTM model. The experimental findings suggest that the BBiLSTM model outperforms other competing models like Long Short-Term Memory (LSTM) network, Support Vector Regression (SVR), Radial Basis Function Neural Network (RBFNN) and Autoregressive Distributed Lag Model (ADLM). The study contributes to tourism literature by proposing a superior deep-learning method for demand forecasting.     

Knowledge mapping of tourism demand forecasting research

Utilizing a scientometric review of global trends and structure from 388 bibliographic records over two decades (1999–2018), this study seeks to advance the building of comprehensive knowledge maps that draw upon global travel demand studies. The study, using the techniques of co-citation analysis, collaboration network and emerging trends analysis, identified major disciplines that provide knowledge and theories for tourism demand forecasting, many trending research topics, the most critical countries, institutions, publications, and articles, and the most influential researchers. The increasing interest and output for big data and machine learning techniques in the field were visualized via comprehensive knowledge maps. This research provides meaningful guidance for researchers, operators and decision makers who wish to improve the accuracy of tourism demand forecasting.     

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.”     

Forecasting tourism demand with multisource big data

Based on internet big data from multiple sources (i.e., the Baidu search engine and two online review platforms, Ctrip and Qunar), this study forecasts tourist arrivals to Mount Siguniang, China. Key findings of this empirical study indicate that (a) tourism demand forecasting based on internet big data from a search engine and online review platforms can significantly improve forecasting performance; (b) compared with tourism demand forecasting based on single-source data from a search engine, demand forecasting based on multisource big data from a search engine and online review platforms demonstrates better performance; and (c) compared with tourism demand forecasting based on online review data from a single platform, forecasting performance based on multiple platforms is significantly better.     

Review of tourism forecasting research with internet data

Internet techniques significantly influence the tourism industry and Internet data have been used widely used in tourism and hospitality research. However, reviews on the recent development of Internet data in tourism forecasting remain limited. This work reviews articles on tourism forecasting research with Internet data published in academic journals from 2012 to 2019. Then, the findings ae synthesized based on the following Internet data classifications: search engine, web traffic, social media, and multiple sources. Results show that among such classifications, search engine data are most widely incorporated into tourism forecasting. Time series and econometric forecasting models remain dominant, whereas artificial intelligence methods are still developing. For unstructured social media and multi-source data, methodological advancements in text mining, sentiment analysis, and social network analysis are required to transform data into time series for forecasting. Combined Internet data and forecasting models will help in improving forecasting accuracy further in future research.     

Novel deep learning approach for forecasting daily hotel demand with agglomeration effect

Increasing competition and adoption of revenue management practices in the hotel industry fuel the need for accurate forecasting to maximize profits and optimize operations. Considering the limitations of relevant research, this study focuses on the daily hotel demand with consideration of agglomeration effect, and proposes a novel deep learning-based model, namely, Deep Learning Model with Spatial and Temporal correlations. This model contributes to relevant research by introducing the agglomeration effect and integrating the attention mechanism and Bayesian optimization algorithm. Historical daily demand data of 210 hotels in Xiamen, China are used to verify the model performance. Results show that the proposed model is significantly better than the benchmarks. This study can help hotel managers improve revenue management through better matching potential demand to available capacity.     

Modelling and forecasting the demand for Hong Kong tourism

The main objectives of this paper are to identify the factors which contribute to the demand for Hong Kong tourism with the aid of econometric models and to generate forecasts of international tourism arrivals to Hong Kong for the period 2001–2008. The general-to-specific modelling approach is followed to model and forecast the demand for Hong Kong tourism by residents from the 16 major origin countries/regions and the empirical results reveal that the most important factors that determine the demand for Hong Kong tourism are the costs of tourism in Hong Kong, the economic condition (measured by the income level) in the origin countries/regions, the costs of tourism in the competing destinations and the ‘word of mouth’ effect. The demand elasticities and forecasts of tourism arrivals obtained from the demand models form the basis of policy formulations for the tourism industry in Hong Kong.     

Tourism implications of online response to terrorism

Individuals all around the world are under constant threat of terrorist attacks. Not surprisingly, terrorist attacks have a strong impact on tourism. However, tourism research is silent on how people respond online after terrorist attacks. Analyzing 154,390 tweets that were posted on Twitter after eight major terrorist attacks that occurred between November 24, 2016 and January 10, 2017, our results demonstrate that people show more anger-related compared to fear-related emotions online after terrorist attacks. We call for further research in tourism to understand how tourism managers and public policy makers can leverage social media after terrorist attacks.     

Tourism demand constraints: A skiing participation

This article provides a critique of the existing literature on leisure constraints. Alternative models are discussed, including the new “negotiation of constraints” model. This study operationalized the new model in order to examine the limiting factors for participants and nonparticipants of skiing. Qualitative analysis revealed that nonskiers faced a number of intrapersonal obstacles, whereas skiers were constrained by time, family, or economic factors. Analysis of questionnaires indicated that economic factors were the major limitations for both groups. When the groups were compared, t-tests indicated that their differences were significantly greater for intrapersonal constraints, supporting the findings from the qualitative research. However, the results did not fully support the new hierarchical model of constraints.     

Predictivity of tourism demand data

As tourism researchers continue to search for solutions to determine the best possible forecasting performance, it is important to understand the maximum predictivity achieved by models, as well as how various data characteristics influence the maximum predictivity. Drawing on information theory, the predictivity of tourism demand data is quantitatively evaluated and beneficial for improving the performance of tourism demand forecasting. Empirical results from Hong Kong tourism demand data show that 1) the predictivity could largely help the researchers estimate the best possible forecasting performance and understand the influence of various data characteristics on the forecasting performance.; 2) the predictivity can be used to assess the short effect of external shock — such as SARS over tourism demand forecasting.