Articlegold2016

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

Chuan Ding · Donggen Wang · Xiaolei Ma · Haiying Li

187 citations0/0 files storedSustainability

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Traffic Prediction and Management Techniques Transportation Planning and Optimization Human Mobility and Location-Based Analysis

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.

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References

2015Long short-term memory neural network for traffic speed prediction using remote microwave sensor data 2014Short-term traffic forecasting: Where we are and where we’re going 2015A gradient boosting method to improve travel time prediction 2010Statistical methods versus neural networks in transportation research: Differences, similarities and some insights 2013Mining smart card data for transit riders’ travel patterns 2011Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks 2008Nature and/or nurture? Analyzing the determinants of transit ridership across US urbanized areas 2015A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system

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