Bikelution: Federated Gradient-Boosting for Scalable Shared Micro-Mobility Demand Forecasting

2026-02-24

AI Summary

This study addresses the challenges of privacy leakage and bandwidth bottlenecks in dockless bike-sharing systems caused by fragmented edge data. To this end, the authors propose a federated learning framework based on gradient-boosted trees for high-accuracy short-to-medium-term (up to six hours ahead) demand forecasting. This work represents the first integration of federated learning with gradient-boosted trees in the shared micromobility domain, enabling collaborative model training across edge devices while preserving user privacy. Experimental results on three real-world datasets demonstrate that the proposed method achieves prediction accuracy comparable to centralized training and outperforms state-of-the-art models, thereby validating the feasibility of efficient, scalable, and privacy-preserving demand forecasting in decentralized urban mobility systems.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• federated learning
• demand forecasting
• bike-sharing
• privacy preservation
• gradient boosting

Innovation

• Federated Learning
• Gradient-Boosted Trees
• Demand Forecasting
• Shared Micro-Mobility
• Privacy-Preserving AI