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

arXiv: https://arxiv.org/abs/2508.16135 · HTML: https://mlsanity.com/paper/2508.16135/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 132 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Builds on (most cited first, 24 of 24)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving](https://mlsanity.com/paper/2112.12610/) (2021)
- [Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions](https://mlsanity.com/paper/1909.00590/) (2019)
- [BEVSegFormer: Bird's Eye View Semantic Segmentation From Arbitrary Camera Rigs](https://mlsanity.com/paper/2203.04050/) (2022)
- [End-to-end Lane Shape Prediction with Transformers](https://mlsanity.com/paper/2011.04233/) (2020)
- [CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention](https://mlsanity.com/paper/2209.07989/) (2022)
- [Lightweight Deep Learning for Resource-Constrained Environments: A Survey](https://mlsanity.com/paper/2404.07236/) (2024)
- [Detection of E-scooter Riders in Naturalistic Scenes](https://mlsanity.com/paper/2111.14060/) (2021)
- [Estimation of energy consumption of electric vehicles using Deep Convolutional Neural Network to reduce driver's range anxiety](https://mlsanity.com/paper/2008.11112/) (2020)
- [A Comparative Study of Using Spatial-Temporal Graph Convolutional Networks for Predicting Availability in Bike Sharing Schemes](https://mlsanity.com/paper/2104.10644/) (2021)
- [Lane-GNN: Integrating GNN for Predicting Drivers' Lane Change Intention](https://mlsanity.com/paper/2207.00824/) (2022)
- [A Review on AI Algorithms for Energy Management in E-Mobility Services](https://mlsanity.com/paper/2309.15140/) (2023)
- [Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles using Federated Learning](https://mlsanity.com/paper/2312.07371/) (2023)
- [AI-Driven Anonymization: Protecting Personal Data Privacy While Leveraging Machine Learning](https://mlsanity.com/paper/2402.17191/) (2024)
- [Detection of Micromobility Vehicles in Urban Traffic Videos](https://mlsanity.com/paper/2402.18503/) (2024)
- [YOLIC: An Efficient Method for Object Localization and Classification on Edge Devices](https://mlsanity.com/paper/2307.06689/) (2023)
- [Optimal Design and Implementation of an Open-source Emulation Platform for User-Centric Shared E-mobility Services](https://mlsanity.com/paper/2403.07964/) (2024)
- [Data-driven Energy Consumption Modelling for Electric Micromobility using an Open Dataset](https://mlsanity.com/paper/2403.17632/) (2024)

## Abstract (arXiv)

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.