Interpretable Tourism Demand Forecasting Considering Multi-source Heterogeneous Big Data under the Impact of Public Health Emergencies

Abstract

Abstract: This study designs a framework for predicting daily tourism demand in natural scenic areas considering the impact of public health emergencies using historical tourism traffic data, the number of confirmed COVID-19 infections, tourism-related and epidemic-related Baidu indices and weather and holiday data. Epidemic-related search engine data are introduced into tourism demand forecasting, and a new ADE-TFT interpretable tourism demand forecasting model is proposed, in which the Adaptive Differential Evolution (ADE) algorithm is used to intelligently and efficiently optimize the time domain temporal fusion transformer (TFT). TFT is an attention-based deep learning model that combines high-performance forecasting with interpretable analysis of temporal dynamics, presenting excellent performance in forecasting studies. The TFT model produces interpretable tourism demand forecasting outputs, including importance ranking of different input variables and attention analysis at different time steps. The interpretable experimental results show that the epidemic-related search engine data can fully reflect the level of tourists’ concerns about the epidemic during the during COVID-19 outbreak, and the findings provide theoretical support for high-precision prediction of tourism demand under the shock of public health emergencies.

Key words: tourism demand forecasting, interpretable forecasting, composite search index, deep learning, public health emergencies

Zeng Yurong, Wu Binrong, Wang Lin, Zhang Jinlong. Interpretable Tourism Demand Forecasting Considering Multi-source Heterogeneous Big Data under the Impact of Public Health Emergencies[J]. Management Review, 2025, 37(2): 139-151.

References

[1] Karabulut G., Bilgin M. H., Demir E., et al. How Pandemics Affect Tourism:International Evidence[J]. Annals of Tourism Research, 2020, 84:102991
[2] Kim Y. R., Liu A. Social Distancing, Trust and Post-COVID-19 Recovery[J]. Tourism Management, 2022, 88:104416
[3] Wickramasinghe K., Ratnasiri S. The Role of Disaggregated Search Data in Improving Tourism Forecasts:Evidence from Sri Lanka[J]. Current Issues in Tourism, 2020, 24(19):2740-2754
[4] 朱亮, 张建萍. 基于Bernstein Copula函数的中国入境旅游需求预测[J]. 旅游学刊, 2017, 32(11):41-48 Zhu L., Zhang J. P. Forecasting Chinese Inbound Tourism Demand with Bernstein Copula Function[J]. Tourism Tribune, 2017, 32(11):41-48
[5] Li H., Hu M., Li G. Forecasting Tourism Demand with Multisource Big Data[J]. Annals of Tourism Research, 2020, 83:102912
[6] Li X., Law R., Xie G., et al. Review of Tourism Forecasting Research with Internet Data[J]. Tourism Management, 2021, 83:104245
[7] 任乐, 崔东佳. 基于网络搜索数据的国内旅游客流量预测研究——以北京市国内旅游客流量为例[J]. 经济问题探索, 2014(4):67-73 Ren L., Cui D. J. Research on Domestic Tourist Flow Prediction Based on Internet Search Data-A Case Study of Beijing's Domestic Tourist Flow[J]. Inquiry Into Economic Issues, 2014, (4):67-73
[8] 王炼, 贾建民. 基于网络信息搜索的旅游需求预测——来自黄金周的证据[J]. 系统管理学报, 2014, 23(3):345-350 Wang L., Jia J. M. Forecasting of Tourism Demand in the Golden Week with Online Information Search[J]. Journal of Systems & Management, 2014, 23(3):345-350
[9] Li X., Law R. Forecasting Tourism Demand with Decomposed Search Cycles[J]. Journal of Travel Research, 2020, 59(1):52-68
[10] Li S., Chen T., Wang L., et al. Effective Tourist Volume Forecasting Supported by PCA and Improved BPNN Using Baidu Index[J]. Tourism Management, 2018, 68:116-126
[11] Peng G., Liu Y., Wang J., et al. Analysis of the Prediction Capability of Web Search Data Based on the HE-TDC Method-Prediction of the Volume of Daily Tourism Visitors[J]. Journal of Systems Science and Systems Engineering, 2017, 26(2):163-182
[12] 李晓炫, 吕本富, 曾鹏志, 等. 基于网络搜索和CLSI-EMD-BP的旅游客流量预测研究[J]. 系统工程理论与实践, 2017, 37(1):106-118 Li X. X., Lv B. F., Zeng Z. P., et al. Tourism Prediction Using Web Search Data Based on CLSI-EMD-BP[J]. System Engineering-Theory & Practice, 2017, 37(1):106-118
[13] Pan B., Yang Y. Forecasting Destination Weekly Hotel Occupancy with Big Data[J]. Journal of Travel Research, 2017, 56(7):957-970
[14] Liu Y., Tseng F., Tseng Y. Big Data Analytics for Forecasting Tourism Destination Arrivals with the Applied Vector Autoregression Model[J]. Technological Forecasting & Social Change, 2018, 130:123-134
[15] Li X., Law R., Xie G., et al. Review of Tourism Forecasting Research with Internet Data[J]. Tourism Management, 2021, 83:104245
[16] Bi J., Li H., Fan Z. Tourism Demand Forecasting with Time Series Imaging:A Deep Learning Model[J]. Annals of Tourism Research, 2021, 90:103255
[17] 梁昌勇, 马银超, 陈荣, 等. 基于SVR-ARMA组合模型的日旅游需求预测[J]. 管理工程学报, 2015, 29(1):122-127 Liang C. Y., Ma Y. C., Chen R. et al. The Daily Forecasting Tourism Demand Based on SVR-ARMA Combination Model[J]. Journal of Industrial Engineering and Engineering Management, 2015, 29(1):122-127
[18] 梁小珍, 张晴, 杨明歌. 面向网络搜索数据的航空客运需求两阶段分解集成预测模型[J]. 管理评论, 2021, 33(5):236-245 Liang X. Z., Zhang Q., Yang M. G. A Two-stage Decomposition Ensemble Model with Internet Search Data for Air Passenger Demand Forecasting[J]. Management Review, 2021, 33(5):236-245
[19] Lim B., Arik S. O., Loeff N., et al. Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting[J]. International Journal of Forecasting, 2021, 37(4):1748-1764
[20] Liu Y., Chen Y., Wu S., et al. Composite Leading Search Index:A Preprocessing Method of Internet Search Data for Stock Trends Prediction[J]. Annals of Operations Research, 2015, 234(1):77-94
[21] Fu C. M., Jiang C., Chen G. S., et al. An Adaptive Differential Evolution Algorithm with an Aging Leader and Challengers Mechanism[J]. Applied Soft Computing, 2017, 57:60-73
[22] Bi J., Liu Y., Li H. Daily Tourism Volume Forecasting for Tourist Attractions[J]. Annals of Tourism Research, 2020, 83:102923
[23] 李洁, 彭其渊, 文超. 基于LSTM深度神经网络的高速铁路短期客流预测研究[J]. 系统工程理论与实践, 2021, 41(10):2669-2682 Li J., Peng Q. Y., Wen C. Short Term Passenger Flow Prediction of High Speed Railway Based on LSTM Deep Neural Network[J]. System Engineering-Theory & Practice, 2021, 41(10):2669-2682
[24] 王会娟, 陈红佳, 高思琴, 等. 基于TEI@I方法论的玉米期货价格预测研究[J]. 管理评论, 2020, 32(7):293-301 Wang H. J., Chen H. J., Gao S. Q., et al. Forecasting Corn Futures Prices Based on TEI@I Methodology[J]. Management Review, 2020, 32(7):293-301