Machine Learning in Micromobility: A Systematic Review of Datasets, Techniques, and Applications
2025-08-25
Summary
The article "Machine Learning in Micromobility: A Systematic Review of Datasets, Techniques, and Applications" provides a detailed overview of how machine learning (ML) is applied in micromobility systems, such as bicycles, e-bikes, and e-scooters. It reviews various datasets, ML models, and their applications in areas like demand prediction, energy management, and safety improvements. The paper also highlights the challenges and future research directions in using ML for enhancing the efficiency and user experience in micromobility.
Why This Matters
Micromobility solutions are crucial for addressing urban challenges like traffic congestion and pollution. Understanding how ML can optimize these systems is vital for city planners and transport operators aiming to create sustainable urban environments. This article fills a gap by systematically reviewing the literature on ML applications in micromobility, offering insights into how these technologies can improve urban transportation infrastructure.
How You Can Use This Info
Professionals involved in urban planning and transportation can leverage the insights from this article to implement and optimize micromobility solutions in their cities. By understanding the ML techniques and datasets discussed, they can better predict demand, manage energy consumption, and enhance safety. This knowledge can guide the integration of advanced technologies into urban transportation systems, improving efficiency and sustainability.