Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (11): 3302-3308.doi: 10.13229/j.cnki.jdxbgxb.20230956

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

Ming-hui YAO(),Wei-chao WANG,Qi-liang WU,Yan NIU   

  1. School of Artificial Intelligence，TianGong University，Tianjin 300387，China
  • Received:2023-09-08 Online:2024-11-01 Published:2025-04-24

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Abstract:

Aiming at the randomness and correlation of passenger flow in urban public transportation hubs， a real-time data feature and XGBoost algorithm based passenger flow prediction for urban public transportation hubs is proposed. Firstly， the automatic encoder is optimized using the AdamOptimizer algorithm， and the real-time passenger flow data feature matrix of urban public transportation hubs is inputted into the optimized automatic encoder to extract data features， Then， an XGBoost model is constructed as the passenger flow prediction model for urban public transportation hubs. Differential evolution algorithm is used to iteratively optimize the model parameters， and the data features are input into the trained XGBoost model to achieve passenger flow prediction for urban public transportation hubs. The experimental results show that the proposed methods have lower RMSE and MAPE， and require less prediction time.

Key words: real time data features, passenger flow forecast, automatic encoder, xgboost algorithm, differential evolution algorithm

CLC Number: 

  • U293.13

Fig.1

Training process of passenger flow prediction model for urban public transportation hubs"

Fig.2

Comparison of root mean square error of prediction results by various methods"

Fig.3

Comparison of average absolute percentage error of prediction results by various methods"

Table 1

Comparison of time used for predictions by various methods"

方法预测时间/s
本文0.18
文献［4］2.05
文献［5］1.29
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