Mobility pattern recognition based prediction for the subway station related bike-sharing trips

The free-floating bike-sharing (BS) system plays an important role in connection with the public transit system. However, few studies have addressed the impacts of the subway network on the BS system and integrated the features quantitatively into the BS trip prediction framework. Based on the observation of the close relationship between the BS and the urban rail transit, our study focuses on the trip forecasting of the BSs around the subway stations. Firstly, the subway station related sites are investigated based on the BS dataset in Beijing, China. Secondly, multiple categories of features are extracted, including the subway station related site categories by clustering, the BS site mobility patterns by tensor decomposition, as well as other features (e.g., temporal, POI, meteorological, and air quality information). Finally, a three-layer ensemble learning model based method (i.e., the SAP-SF method) under the stacking strategy is proposed with integrations of multiple features and the several selected machine learning algorithms. It is applied to the simultaneous prediction of the hourly return numbers for a large-scale BS system involving a total of 280 sites in Beijing. The output performance is also examined by comparing the results with those obtained from the benchmark models. It is indicated that the features of subway station related site categories and site mobility patterns jointly contribute to the improvement of BS trip prediction. The accuracy can be increased layer by layer and is superior to the single machine learning algorithm. The research finding can provide useful information for system administrators to perform service level checks and to rebalance BSs around subway stations.

Automated summary

This study focuses on predicting the hourly return numbers for a large-scale bike-sharing system around subway stations in Beijing, China. By integrating multiple features and machine learning algorithms in a three-layer ensemble learning model, the accuracy of the prediction is improved, outperforming single machine learning algorithms. The research findings can provide valuable information for system administrators to evaluate service levels and rebalance bikes around subway stations.