Yunpeng Li

Demand forecasting is important for management and decision making in the tourism sector. However, research on deep learning forecasting, which combines multiple data sources, is still in the development phase. This study proposes a bidirectional long short-term memory (BiLSTM) forecasting method incorporating an attention mechanism (ATT-BiLSTM) that can better extract data features from a set of predictor variables consisting of multiple predictor variables (generated from historical tourist volume, search engine data, weather data and day off data). The research experimentally validates the effectiveness of the method using the famous Chinese tourist attraction Jiuzhaigou as a case study. The results show that the proposed model not only has better generalization ability but also significantly outperforms the four benchmark models, convolutional neural network (CNN), SVR, LSTM, and BiLSTM, in terms of prediction accuracy. In addition, we analyse the importance of the different predictor variables in the prediction model characteristics.

Smart tourism and associated topics have been extensively discussed by scholars around the world. The goal of this study is to make available an all-inclusive database-based analysis of the longitude status of research on smart tourism. Three databases, Web of Science (WoS), Science Direct and China Knowledge Network (CNKI), were utilized to gather papers published from 2011 to 2020. The data results were analyzed and results were generated using CiteSpace. The results of Chinese and English papers were evaluated to form the conclusion of this study. The implication formed the prediction of future research trends and development suggestions.