Abstract—As a solution to the last mile problem in big
metropolitan cities, free-floating bike-sharing service is
becoming a new choice for short travels all over the world.
Unlike the docked bikes which requires the users to borrow
and return at fixed stations, free-floating bikes can be used
everywhere. However, this feasibility also brings a higher
management cost. The bikes should be scheduled from the
regions with less demand to those with higher demand, based
on a precise demand prediction. In this paper, we use deep
learning techniques including Multi-Layer Perceptron and
ConvLSTM networks for this task. We find that in the case of
the insufficient training data, e.g., one-month data of Mobike,
Multi-Layer Perceptron performs better than both
ConvLSTM and two simple historical methods.
Index Terms—Free-floating bike-sharing, demand
prediction, deep learning.
Ziyang Zhang and Lingye Tan are with the Faculty of Engineering,
University of New South Wales, Sydney, 2032, Australia (e-mail:
zzy967230@163.com, tanlingyelynn@163.com).
Weiwei Jiang is with the Department of Electronic Engineering,
Tsinghua University, Beijing, People’s Republic of China (Corresponding
author; e-mail: jwwthu@163.com).
Cite: Ziyang Zhang, Lingye Tan, and Weiwei Jiang, "Free-Floating Bike-Sharing Demand Prediction with Deep Learning," International Journal of Machine Learning and Computing vol. 12, no. 2, pp. 63-67, 2022.
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