Search Results (5)

Accurate forecasting of tourist volume is crucial for the sustainable development of the tourism industry. Deep-learning methods based on multivariate data can enhance the accuracy of tourism demand forecasting, enabling tourism management departments and enterprises to make evidence-based decisions. This study adopts an inverted transformer approach with a self-attention mechanism, which can improve the extraction of correlation features from the time series of multiple variables. Taking Zhejiang Province, a major tourist destination in China, and Hangzhou, a famous tourist city in China, as case studies, this research considers historical tourist volume, search engine data, weather data, date pattern data, and seasonal data in daily tourism volume forecasting. By comparing the forecasting results with three benchmark models, including CNN, RNN, and LSTM, the inverted transformer model’s effectiveness in forecasting the daily total visitors and overnight visitors is validated. This study’s findings can be applied to forecast the regional daily tourist arrivals, enabling decision-makers in the tourism sector to make more precise forecasts and devise more dependable plans. Full article

Contemporary techniques built on deep learning technologies enable precise forecasting of tourism demand, particularly for the relaunch of sustainable tourism following COVID-19. We developed a novel framework to forecast visitor arrivals at tourist attractions in the post-COVID-19 period. To this end, a time-based data partitioning module was first pioneered. The N-BEATS algorithm with multi-step strategies was then imported to build a forecasting system on historical data. We imported visualization of curve fitting, metrics of error measures, wide-range forecasting horizons, different strategies for data segmentations, and the Diebold–Mariano test to verify the robustness of the proposed model. The system was empirically validated using 1604 daily visitor volumes of Jiuzhaigou from 1 January 2020 to 13 May 2024 and 1459 observations of Mount Siguniang from 1 October 2020 to 18 May 2024. The proposed model achieved an average MAPE of 39.60% and MAAPE of 0.32, lower than the five baseline models of SVR, LSTM, ARIMA, SARIMA, and TFT. The results show that the proposed model can accurately capture sudden variations or irregular changes in the observations. The findings highlight the importance of improving destination management and anticipatory planning using the latest time series approaches to achieve sustainable tourist visitation forecasts. Full article

Public crises can bring unprecedented damage to the tourism industry and challenges to tourism demand forecasting, which is essential for crisis management and sustainable development. Existing studies mainly focused on point forecasts, but point forecasts may not be enough for the uncertain environments of public crises. This study proposes a combined Bayesian interval tourism demand forecasting model based on a forgetting curve. Moreover, considering tourists’ travel plans may be adjusted due to changing crisis situations, the choice of search engine data for forecasting tourism demand is investigated and incorporated into the proposed model to yield reliable results. Through an empirical study, this study figures out that the Baidu Index had better tourism predictive capabilities before the public crisis, whereas the Google Index effectively captured short-term fluctuations of tourism demand within the crisis period. The results also indicate that integrating both Baidu and Google Index data obtains the best prediction performance after the crisis outbreak. Our main contribution is that this study can generate flexible forecasting results in the interval form, which can effectively handle uncertainties in practice and formulate control measures for practitioners. Another novelty is successfully discovering how to select appropriate search engine data to improve the performance of tourism demand forecasts across different stages of a public crisis, thus benefiting daily operations and crisis management in the tourism sector. Full article

Overnight forecasting is a crucial challenge for revenue managers because of the uncertainty associated between demand and supply. However, there is limited research that focuses on predicting daily hotel demand. Hence, this paper evaluates various models’ of traditional time series forecasting performances for daily demand at multiple horizons. The models include the seasonal naïve, Holt–Winters (HW) triple exponential smoothing, an autoregressive integrated moving average (ARIMA), a seasonal autoregressive integrated moving average (SARIMAX) with exogenous variables, multilayer perceptron (MLP) artificial neural networks model (ANNs), an sGARCH, and GJR-GARCH models. The dataset of this study contains daily demand observations from a hotel in a US metropolitan city from 2015 to 2019 and a set of exogenous social and environmental features such as temperature, holidays, and hotel competitive set ranking. Experimental results indicated that under the MAPE accuracy measure: (i) the SARIMAX model with external regressors outperformed the ANN-MLP model with similar external regressors and the other models, in every one horizon except one out of seven forecast horizons; (ii) the sGARCH(4, 2) and GJR-GARCH(4, 2) shows a superior predictive accuracy at all horizons. The results performance is evaluated by conducting pairwise comparisons between the different model’s distribution of forecasts using Diebold–Mariano and Harvey–Leybourne–Newbold tests. The results are significant for revenue managers because they provide valuable insights into the exogenous variables that impact accurate daily demand forecasting. Full article

The Chinese tourism industry has been developing rapidly for the past several years, and the number of people traveling has been increasing year by year. However, many problems still beset current tourism management. Lack of effective management has caused numerous problems, such as tourists stranded during tourist season and the declining service quality of scenic spots, which have become the focus of tourists’ attention. Network search data can intuitively reflect the attention of most users through the combination of the network search index and the back propagation (BP) neural network model. This study predicts the daily tourism demand in the Huangshan scenic spot in China. The filtered keyword in the Baidu index is added to the hybrid neural network, and a BP neural network model optimized by a fruit fly optimization algorithm (FOA) based on the web search data is established in this study. Different forecasting methods are compared in this paper; the results prove that compared with other prediction models, higher accuracy can be obtained when it comes to the peak season using the FOA-BP method that includes web search data, which is a sustainable means of practically solving the tourism management problem by a more accurate prediction of tourism demand of scenic spots. Full article