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Mitigating the disturbances of events on tourism demand forecasting

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Abstract

Developing an accurate tourism forecasting decision support system can help the tourism department achieve optimal resource allocation, which is crucial for achieving sustainable tourism operation management and a circular economy. Recent decades have witnessed the frequent strikes of crisis events and mega-events, which profoundly influence tourist arrival volume and bring a great challenge to forecasting tourist arrival volume. To solve this issue, we develop a deep learning framework to forecast the tourist arrival volume utilizing search engine data containing the trends of tourism intention and different event information. Our proposed model is novel for the following reasons: (1) The disturbance value can predict tourist arrival volume in coordination with the trend of travel plans. (2) Compared with the traditional models, our model can reduce the complexity of the model while maintaining accuracy. (3) Our proposed framework introducing event-related search volumes can capture the concerns of tourists and the potential loss of tourist arrivals, enhancing the model’s predictive power. Experimental results show that our model can accurately forecast the tourist arrival volume by employing the monthly data in Beijing and Sanya, China. Moreover, our findings provide policymakers with more understanding of the relationship between various predictive factors and tourist arrivals. Based on the forecasting results, allocating an appropriate amount of clean energy transportation capacity, garbage treatment capacity, and fresh food supply capacity to the city can effectively promote the circular economy.

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Acknowledgements

The authors thank the anonymous referees for helpful comments. Their constructive comments will help to improve the quality of the paper greatly.

Funding

National Natural Science Foundation of China (Grant No. 62276020), Beijing Natural Science Foundation (Grant No. 9222025), MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Grant No. 19YJC630043).

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  1. School of Economics and Management, Beijing Jiaotong University, Beijing, 100044, China

    Tairan Zhang, Zhenji Zhang & Gang Xue

  2. School of Economics and Management, Tsinghua University, Beijing, 100084, China

    Gang Xue

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  1. Tairan Zhang
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Correspondence to Gang Xue.

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There are no financial or non-financial interests that are directly or indirectly related to the work submitted for publication. Author Tairan Zhang declares that he has no conflict of interest. Zhenji Zhang declares that he has no conflict of interest. Author Gang Xue declares that he has no conflict of interest.

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Zhang, T., Zhang, Z. & Xue, G. Mitigating the disturbances of events on tourism demand forecasting. Ann Oper Res 342, 1019–1040 (2024). https://doi.org/10.1007/s10479-023-05626-6

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