| dc.description.abstract | MRT passenger flow forecasting is an important part of an advanced traffic information system, assisting MRT authorities in performing ticket distribution, operation planning, revenue management, MRT station management, marketing plan planning, etc., or assisting companies in emergency management in extreme cases. Many domestic researches try to apply parametric machine learning models and deep learning models to perform passenger flow forecasting. However, parametric machine learning models have certain limitations with the increase of data, and deep learning training models are quite time-consuming. Today ensemble learning is widely used in foreign research and artificial intelligence competitions. In this study, three integrated learning models, Random Forest, AdaBoost, and XGBoost, are proposed to compare with the Neural Network deep learning model.
Station passenger flow is greatly affected by various factors such as cycles, holidays, off-peak hours, special festivals or large-scale events. Extracting key features from data is crucial for passenger flow prediction models. The Random Forest and XGBoost models proposed in this study can achieve better prediction accuracy and computational efficiency on real-world datasets. In addition, deleting the two outliers of large activities and special festivals can get better prediction results, while adding the weather factor of rainfall has little effect on the forecast of Taoyuan MRT passenger flow. | en_US |