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Tourist Trajectory Prediction Based on Improved LightGBM
2022 International Conference on Statistics, Data Science, and Computational Intelligence, CSDSCI 2022 ; 12510, 2023.
Article in English | Scopus | ID: covidwho-2237563
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. © 2023 SPIE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study / Qualitative research / Randomized controlled trials Language: English Journal: 2022 International Conference on Statistics, Data Science, and Computational Intelligence, CSDSCI 2022 Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study / Qualitative research / Randomized controlled trials Language: English Journal: 2022 International Conference on Statistics, Data Science, and Computational Intelligence, CSDSCI 2022 Year: 2023 Document Type: Article