Hyper-clustering enhanced spatio-temporal deep learning for traffic and demand prediction in bike-sharing systems

Abstract

Bike traffic prediction is essential to operations management in bike-sharing systems (BSS). The spatio-temporal distribution of shared bikes is imbalanced; therefore, it is necessary to predict the number of bikes that are available at each station to facilitate operations. Many researchers have developed deep neural network methods to improve predictive performance. Predicting bike demand from a higher level by clustering mobility trends has been shown to increase prediction accuracy. However, complex iterative optimization problems need to be solved to stabilize the trend iteration, and such solutions are missing in previous works. In this paper, we propose a novel hyper-clustering approach that enhances a spatio-temporal deep neural network for traffic prediction in bike-sharing systems. In particular, our hyper-clustering approach captures mobility trends among individuals and clusters. Experimental results show that the designed hyper-clustering-enhanced spatio-temporal deep neural network model can predict the number of available bikes more accurately. Compared with ten state-of-the-art benchmark methods, our model demonstrates better performance in reducing prediction error by 10.9%–43.1%.