Use of Deep Learning to Predict Daily Usage of Bike Sharing Systems

Abstract

The use of bikes among stations is often spatiotemporally imbalanced, causing many problems in daily operations. Predictively knowing how the system demand evolves in advance helps improve the preparedness of operational schemes. This paper aims to present a predictive modeling approach to analyze the use of bicycles in bike sharing systems. Specifically, a deep learning (DL) approach using the convolutional neural networks (CNNs) was proposed to predict the daily bicycle pickups at both city and station levels. A numerical study using data from the Citi Bike system in New York City (NYC) was performed to assess the performance of the proposed approach. Other than the historical records, relevant information like weather was also incorporated in the modeling process. The modeling results show that the proposed approach can achieve improved predictive performance in both city- and station-level analyses, confirming the merits of the proposed method against other baseline approaches. In addition, including information from neighboring stations into the models can help improve the performance of station-level prediction. The predictive performance of the CNN was also found to be related to parameters such as temporal window, number of neighboring stations, learning ratio, patch size, and the inclusion of additional data such as drop-offs. Thus, the implementation of the proposed models requires necessary calibration to determine appropriate parameters for a given bike sharing system.