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IJMLC 2022 Vol.12(2): 63-67 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2022.12.2.1080

Free-Floating Bike-Sharing Demand Prediction with Deep Learning

Ziyang Zhang, Lingye Tan, and Weiwei Jiang

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).

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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.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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