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Sustainability 2016, 8(11), 1100; doi:10.3390/su8111100

Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees

1
School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control, Beihang University, Beijing 100191, China
2
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
3
Department of Geography, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
4
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Si-Pai-Lou #2, Nanjing 210096, China
*
Author to whom correspondence should be addressed.
Academic Editor: Marc A. Rosen
Received: 5 September 2016 / Revised: 18 October 2016 / Accepted: 21 October 2016 / Published: 28 October 2016
View Full-Text   |   Download PDF [1114 KB, uploaded 28 October 2016]   |  

Abstract

Understanding the relationship between short-term subway ridership and its influential factors is crucial to improving the accuracy of short-term subway ridership prediction. Although there has been a growing body of studies on short-term ridership prediction approaches, limited effort is made to investigate the short-term subway ridership prediction considering bus transfer activities and temporal features. To fill this gap, a relatively recent data mining approach called gradient boosting decision trees (GBDT) is applied to short-term subway ridership prediction and used to capture the associations with the independent variables. Taking three subway stations in Beijing as the cases, the short-term subway ridership and alighting passengers from its adjacent bus stops are obtained based on transit smart card data. To optimize the model performance with different combinations of regularization parameters, a series of GBDT models are built with various learning rates and tree complexities by fitting a maximum of trees. The optimal model performance confirms that the gradient boosting approach can incorporate different types of predictors, fit complex nonlinear relationships, and automatically handle the multicollinearity effect with high accuracy. In contrast to other machine learning methods—or “black-box” procedures—the GBDT model can identify and rank the relative influences of bus transfer activities and temporal features on short-term subway ridership. These findings suggest that the GBDT model has considerable advantages in improving short-term subway ridership prediction in a multimodal public transportation system. View Full-Text
Keywords: short-term subway ridership prediction; gradient boosting decision tree; bus transfer activities; multimodal public transportation; variable importance short-term subway ridership prediction; gradient boosting decision tree; bus transfer activities; multimodal public transportation; variable importance
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Ding, C.; Wang, D.; Ma, X.; Li, H. Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees. Sustainability 2016, 8, 1100.

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