Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 30, 24 January 2024
Open
Access | Article
Using machine learning for bike sharing demand prediction
* Author to whom correspondence should be addressed.
Abstract
Bike sharing has become a much more popular topic nowadays. Not only do the producers in bike-sharing need to provide a relatively accurate number of bikes in each period, but also the consumers need to have a general understanding of the number of bikes in each hour. This study analyses the dataset of bike-sharing rentals in 2011 in Washington, D.C. using machine learning, after training, testing, analyzing, and visualizing the dataset, the author chose the best model-random forest to predict it through the method of cross-test. The research result shows that the number of rentals in bike-sharing is the highest in the morning and evening travel peaks in one day, the highest in working days in one week, and the highest in autumn in one year. This information can help the bike-sharing service to prepare different quantities of bike-sharing at different times, and the customers would have a better overview of the bike demand when they plan to rent one. The whole research process provides valuable information for the service providers and users of bike-sharing.
Keywords
Bike-sharing, machine learning, prediction
References
1. "The Many Benefits of Bike Sharing Programs", 2016, (Commute Options), Retrieved 27 August 2021.
2. Kodukula, S. 2010, "Recommended Reading and Links on Public Bicycle Schemes", (European Commission), Retrieved 7 August 2019.
3. Mattlamy. 2019. "A guide to hire bikes and public bike share schemes", (Cycling UK).
4. Porter, J. 2020, "Google Maps now shows cycling routes using docked bike-sharing schemes", (The Verge), Retrieved 24 July 2020.
5. Sathishkumar, V. E. Jangwoo, P. Yong, Y. C. 2020, Using data mining techniques for bike sharing demand prediction in metropolitan city, (Computer Communications, vol. 153), pp. 353-366.
6. V, E. S. Cho, Y. 2020, Season wise bike sharing demand analysis using random forest algorithm, (Computational Intelligence), pp. 1-26.
7. Wang, X.D. Cheng, Z. H. Martin, T. Sun, L. J. 2021, Modeling bike-sharing demand using a regression model with spatially varying coefficients, (Journal of Transport Geography, vol. 93), no. 103059.
8. Cantelmo, G. Kucharski, R. Antoniou, C. 2020, Low-Dimensional Model for Bike-Sharing Demand Forecasting that Explicitly Accounts for Weather Data, (Transportation Research Record, vol. 2674), no. 8, pp. 132-144.
9. Zi,W. J. Xiong, W. Chen, H. Chen, L. 2021, Station-level demand prediction for bike-sharing system via a temporal attention graph convolution network, (Information Sciences, vol. 561), pp. 274-285.
10. Chang, P. C. Wu, J. L. Xu, Y. et al. 2019, Bike sharing demand prediction using artificial immune system and artificial neural network, (Soft Comput, vol. 23), pp. 613–626.
11. Xiao, G. Wang, R. Zhang, C. et al. 2021, Demand prediction for a public bike sharing program based on spatio-temporal graph convolutional networks, (Multimed Tools Appl, vol. 80), pp. 22907–22925.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-283-1
- ISBN (Online)
- 978-1-83558-284-8
- Published Date
- 24 January 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/30/20241078
- Copyright
- 24 January 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited