Bike-Sharing Demand Forecasting Using Machine Learning

Authors

DOI:

https://doi.org/10.62161/sauc.v11.6024

Keywords:

Bike-sharing, Demand forecasting, Supervised learning, Unsupervised learning, Machine learning, Simulation

Abstract

Bike-share systems work best when users can count on finding a bike and an open dock. That simple expectation is hard to meet during peak times and special events. We describe a practical, short-term demand-forecasting pipeline built using Machine Learning (ML) and deployed in Valladolid, Spain, as part of the EU-funded SPINE project. Using anonymized station-to-station records from the city’s bike-sharing system, we generate station-level forecasts over 60-minute horizons and feed those predictions to operations dashboards. Moreover, rebalancing strategies are evaluated in a simulation environment. We summarize the data flow and model architecture, show representative network-level results, and outline how the system can be used by the bike-sharing operator to support day-to-day decisions. We close with lessons learned and priorities for the next steps.

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References

Gammelli, D., Wang, Y., Prak, D., Rodrigues, F., Minner, S., & Pereira, F. C. (2022). Predictive and prescriptive performance of bike-sharing demand forecasts for inventory management. Transportation Research Part C: Emerging Technologies, 138, 103571. https://doi.org/10.1016/j.trc.2022.103571

Gao, C., & Chen, Y. (2022). Using machine learning methods to predict demand for bike sharing. In J. L. Stienmetz, B. Ferrer-Rosell, & D. Massimo (Eds.), Information and Communication Technologies in Tourism 2022 (pp. 282–296). Springer. https://doi.org/10.1007/978-3-030-94751-4_25

Jiang, W. (2022). Bike sharing usage prediction with deep learning: A survey. Neural Computing and Applications, 34, 15369–15385. https://doi.org/10.1007/s00521-022-07380-5

Li, X., Xu, Y., Zhang, X., Shi, W., Yue, Y., & Li, Q. (2023). Improving short-term bike sharing demand forecast through an irregular convolutional neural network. Transportation Research Part C: Emerging Technologies, 147, 103984. https://doi.org/10.1016/j.trc.2022.103984.

Martín, I., Tympakanaki, A., Cantú-Ros, O. G., Burrieza-Galán, J., Casas, J., Narayanan, S., Ayfantopoulou, G., Antoniou, C., & Maleas, Z. (2023). Data-driven modelling and simulation evaluation of shared mobility services: A case study for bike-sharing in Madrid. 102nd Annual Meeting of the Transportation Research Board. Washington, DC.

Peláez-Rodríguez, C., Pérez-Aracil, J., Fister, D., Torres-López, R., & Salcedo-Sanz, S. (2024). Bike sharing and cable car demand forecasting using machine learning and deep learning multivariate time series approaches. Expert Systems with Applications, 238, 122264. https://doi.org/10.1016/j.eswa.2023.122264

Qiao, S., Han, N., Huang, J., Yue, K., Mao, R., Shu, H., He, Q., & Wu, X. (2021). A dynamic convolutional neural network based shared-bike demand forecasting model. ACM Transactions on Intelligent Systems and Technology, 12(6), Article 70. https://doi.org/10.1145/3447988

Subramanian, M., Cho, J., Veerappampalayam Easwaramoorthy, S., Murugesan, A., & Chinnasamy, R. (2023). Enhancing sustainable transportation: AI-driven bike demand forecasting in smart cities. Sustainability, 15(18), 13840. https://doi.org/10.3390/su151813840

Sohrabi, S., Paleti, R., Balan, L., & Cetin, M. (2020). Real-time prediction of public bike sharing system demand using generalized extreme value count model. Transportation Research Part A: Policy and Practice, 133, 325–336. https://doi.org/10.1016/j.tra.2020.02.001

Yang, Y., Heppenstall, A., Turner, A., & Comber, A. (2020). Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems. Computers, Environment and Urban Systems, 83, 101521. https://doi.org/10.1016/j.compenvurbsys.2020.101521

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Published

2025-12-01

How to Cite

Angarita-Zapata, J. S., Torrent-Fontbona , F., Machón , B., & Benito Sanchez, F. (2025). Bike-Sharing Demand Forecasting Using Machine Learning. Street Art & Urban Creativity, 11(7), 329–342. https://doi.org/10.62161/sauc.v11.6024

Issue

Vol. 11 No. 7 (2025): Special Issue: Smart City World Congress

Section

Special Issue SmartCityExpo

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