Deep Learning Based Prediction of Transfer Probability of Shared Bikes Data

Abstract

In the pile-free bicycle sharing scheme, the parking place and time of the bicycle are arbitrary. The distribution of the pile does not constrain the origin and destination of the journey. The travel demand of the user can be derived from the use of the shared bicycle. The goal of this article is to predict the probability of transition for a shared bicycle user destination based on a deep learning algorithm and a large amount of trajectory data. This study combines eXtreme Gradient Boosting (XGBoost) algorithm, stacked Restricted Boltzmann Machines (RBM), support vector regression (SVR), Differential Evolution (DE) algorithm, and Gray Wolf Optimization (GWO) algorithm. In an experimental case, the destinations of the cycling trips and the probability of traffic flow transfer for shared bikes between traffic zones were predicted by computing 2.46 million trajectory points recorded by shared bikes in Beijing. The hybrid algorithm can improve the accuracy of prediction, analyze the importance of various factors in the prediction of transfer probability, and explain the travel preferences of users in the pile free bicycle-sharing scheme.