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13 January 2023 Tourist trajectory prediction based on improved LightGBM
Debin Zhao, Zhengyuan Hu, Yinjian Yang
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Proceedings Volume 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022); 125100A (2023) https://doi.org/10.1117/12.2656788
Event: International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 2022, Qingdao, China
Abstract
Considering the influences of the COVID-19 disease, systemic risks with respect to the tourism industry and the erratic preferences of the tourists have fiercely affected the performance of machine learning models for tourist trajectory prediction. This paper introduces a noise-reduced and Bayesian optimized light gradient boosting machine (LightGBM) to forecast the likelihood of visitors entering the consequent scenic attraction, accommodating to the variability of tourism attributes. The empirical evidence of tourism data in Luoyang City Hall from March 2020 to November 2021 illustrates that our practice surpasses the baseline LightGBM mechanism as well as a random search-based technique regarding prediction loss by 5.39% and 4.42% correspondingly. The proposed research demonstrates a promising stride in the improvement of intelligent tourism in the experimental area by enhancing tourist experiences and allocating tourism resources efficiently, which can also be smoothly applied to other scenic spots.
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Debin Zhao, Zhengyuan Hu, and Yinjian Yang "Tourist trajectory prediction based on improved LightGBM", Proc. SPIE 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 125100A (13 January 2023); https://doi.org/10.1117/12.2656788
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KEYWORDS
Data modeling
Machine learning
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