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

TL;DR

This paper systematically reviews datasets, machine learning techniques, and applications in micromobility systems, highlighting current challenges and future research directions to enhance efficiency, safety, and user experience.

Contribution

It provides the first comprehensive review of ML applications in micromobility, covering datasets, models, and specific use cases, filling a significant literature gap.

Findings

Analyzed diverse micromobility datasets with spatial and temporal features.

Reviewed ML models used for demand prediction, energy management, and safety.

Identified challenges and proposed future research directions in the field.

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.