Recently, dockless sharing-bikes systems are widely developed in many countries and have been studied extensively within the transport domain. This study aimed to develop a dynamic travel demand prediction model for dockless sharing bikes by using the deep learning method. Firstly, the geographical algorithm was conducted to finish the division of traffic analysis zones (TAZ), considering the spatial correlation of sharing bicycle trips. Analyses on cycling trips were accomplished to present the imbalance of spatial and temporal distribution. Additionally, the temporal convolutional networks (TCN), which constantly performs well on a vast range of tasks, were then applied to predict the demand over the TAZs. The statistical model and other machine learning models were then developed to benchmark the TCN forecast model. The comparison results confirmed the TCN model’s advantages beyond baseline approaches. Finally, the models provided the prediction of the gap between the production and attraction at each TAZ, which provides useful strategies for rebalancing the dockless sharing-bicycles system.

CICTP