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

Shengjie Zhao, Kai Zhao, Yusen Xia, Wenzhen Jia

Abstract: Highlights•We propose a hyper-clustering (hyper-AP clustering) approach to capture the migration trend between individuals and clusters in intelligent transportation systems.•We use the hyper-clustering enhanced spatio-temporal deep learning model such as ST-ResNet and ST-3DNet, and we show that our approach can significantly improve the prediction accuracy of both deep learning methods.•Compared with state-of-the-art methods, the improvement of the prediction performance of our model is 10.9