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Passenger flow prediction of urban public transportation hubs based on real-time data features and XGBoost algorithm
更新时间：2025-07-21

    • Passenger flow prediction of urban public transportation hubs based on real-time data features and XGBoost algorithm

    • “In the field of predicting passenger flow in urban public transportation hubs, experts have proposed a prediction method based on real-time data features and XGBoost algorithm, optimizing autoencoders to extract data features, and using differential evolution algorithm to iteratively optimize model parameters to achieve passenger flow prediction. The experimental results show that this method has lower RMSE and MAPE, and requires less time for prediction.”
    • Journal of Jilin University(Engineering and Technology Edition)   Vol. 54, Issue 11, Pages: 3302-3308(2024)
    • 作者机构：

      天津工业大学 人工智能学院，天津 300387

    • DOI：10.13229/j.cnki.jdxbgxb.20230956    

      CLC： U293.13

    • Received：08 September 2023，

      Published：01 November 2024

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  • YAO Ming-hui,WANG Wei-chao,WU Qi-liang,et al.Passenger flow prediction of urban public transportation hubs based on real-time data features and XGBoost algorithm[J].Journal of Jilin University(Engineering and Technology Edition),2024,54(11):3302-3308. DOI： 10.13229/j.cnki.jdxbgxb.20230956.

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