![[2602.20671] Bikelution: Federated Gradient-Boosting for Scalable Shared Micro-Mobility Demand Forecasting](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.20671] Bikelution: Federated Gradient-Boosting for Scalable Shared Micro-Mobility Demand Forecasting
Summary
The paper presents Bikelution, a federated learning approach for predicting demand in shared micro-mobility systems, addressing privacy concerns while maintaining accuracy.
Why It Matters
As urban mobility evolves with dockless bike-sharing systems, accurate demand forecasting becomes crucial for efficient fleet management. Bikelution offers a privacy-preserving solution that leverages distributed data, making it relevant for cities aiming for sustainable transport solutions.
Key Takeaways
- Bikelution utilizes federated learning to enhance demand forecasting accuracy while preserving user privacy.
- The model achieves comparable results to centralized machine learning methods without the associated privacy risks.
- Real-world experiments validate Bikelution's effectiveness across multiple datasets.
- The study highlights trade-offs between federated and centralized approaches in machine learning.
- This research supports the development of sustainable urban mobility solutions.
Computer Science > Machine Learning arXiv:2602.20671 (cs) [Submitted on 24 Feb 2026] Title:Bikelution: Federated Gradient-Boosting for Scalable Shared Micro-Mobility Demand Forecasting Authors:Antonios Tziorvas, Andreas Tritsarolis, Yannis Theodoridis View a PDF of the paper titled Bikelution: Federated Gradient-Boosting for Scalable Shared Micro-Mobility Demand Forecasting, by Antonios Tziorvas and 2 other authors View PDF HTML (experimental) Abstract:The rapid growth of dockless bike-sharing systems has generated massive spatio-temporal datasets useful for fleet allocation, congestion reduction, and sustainable mobility. Bike demand, however, depends on several external factors, making traditional time-series models insufficient. Centralized Machine Learning (CML) yields high-accuracy forecasts but raises privacy and bandwidth issues when data are distributed across edge devices. To overcome these limitations, we propose Bikelution, an efficient Federated Learning (FL) solution based on gradient-boosted trees that preserves privacy while delivering accurate mid-term demand forecasts up to six hours ahead. Experiments on three real-world BSS datasets show that Bikelution is comparable to its CML-based variant and outperforms the current state-of-the-art. The results highlight the feasibility of privacy-aware demand forecasting and outline the trade-offs between FL and CML approaches. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2602.20671 [cs.LG] (or arXiv:2602.206...
