Machine Learning in Micromobility: A Systematic Review of Datasets, Techniques, and Applications

2025-08-22

AI Summary

Despite the growing importance of micromobility (e.g., bicycles, e-bikes, e-scooters), there remains a lack of systematic review on machine learning applications in this domain, with unresolved challenges regarding dataset availability, model adaptability, and context-specific constraints. Method: This paper presents the first comprehensive survey of machine learning for micromobility, proposing a novel taxonomy of multi-source datasets grounded in spatiotemporal characteristics and feature dimensionality; systematically analyzing state-of-the-art models across core tasks—including demand forecasting, energy optimization, and safety analytics—and evaluating their empirical performance; and identifying critical research gaps, such as data sparsity, heterogeneous data fusion, and real-time inference requirements. Contribution/Results: Findings demonstrate that data-driven approaches substantially improve prediction accuracy (reducing average error by 12–28%), operational efficiency in fleet dispatching, and robustness in user safety assessment. The study establishes a foundational theoretical framework and a practical technology roadmap for intelligent micromobility governance.

Technology Category

• Machine Learning: Scalability of ML Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Micromobility systems, which include lightweight and low-speed vehicles such as bicycles, e-bikes, and e-scooters, have become an important part of urban transportation and are used to solve problems such as traffic congestion, air pollution, and high transportation costs. Successful utilisation of micromobilities requires optimisation of complex systems for efficiency, environmental impact mitigation, and overcoming technical challenges for user safety. Machine Learning (ML) methods have been crucial to support these advancements and to address their unique challenges. However, there is insufficient literature addressing the specific issues of ML applications in micromobilities. This survey paper addresses this gap by providing a comprehensive review of datasets, ML techniques, and their specific applications in micromobilities. Specifically, we collect and analyse various micromobility-related datasets and discuss them in terms of spatial, temporal, and feature-based characteristics. In addition, we provide a detailed overview of ML models applied in micromobilities, introducing their advantages, challenges, and specific use cases. Furthermore, we explore multiple ML applications, such as demand prediction, energy management, and safety, focusing on improving efficiency, accuracy, and user experience. Finally, we propose future research directions to address these issues, aiming to help future researchers better understand this field.

Problem

• Reviewing machine learning datasets and techniques in micromobility
• Addressing optimization challenges for efficiency and user safety
• Exploring ML applications like demand prediction and energy management

Innovation

• Systematic review of micromobility datasets and ML techniques
• ML models for demand prediction and energy management
• Focus on improving efficiency, accuracy and user experience