MobiML is a Python framework for machine learning from movement data and part of the MovingPandas ecosystem.

For API documentation and source code, visit the GitHub repository.

Installation

Note: MobiML requires Linux (one of its main dependencies, pymeos, is not yet available on Windows).

Install uv, clone the repository, then:

uv sync

In your application’s pyproject.toml:

[tool.hatch.metadata]
allow-direct-references = true

Then install:

uv add ../my/local/mobiml

Modules

MobiML contains modules for learning and data preprocessing from movement data:

• datasets — Classes for handling popular movement datasets
• models — Models for a variety of mobility-related ML tasks
• preprocessing — Tools to preprocess movement data into mobiml.Dataset objects, ready for ML development
• samplers — Tools for sampling movement data while accounting for spatiotemporal characteristics
• transforms — Transformation operations that convert a mobiml.Dataset into different data structures

Included Models

• GeoTrackNet — Anomaly detection in maritime traffic patterns, based on GeoTrackNet. Nguyen et al. (2022). GeoTrackNet - A Maritime Anomaly Detector Using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection. IEEE Transactions on Intelligent Transportation Systems, 23(6). arXiv:1912.00682

• Nautilus — Vessel Route Forecasting, based on Nautilus. Tritsarolis et al. (2024). On Vessel Location Forecasting and the Effect of Federated Learning. MDM 2024. arXiv:2405.19870

• SummarizedAISTrajectoryClassifier — A basic example model implementing Logistic Regression for trajectory classification in a federated learning setting

Examples

Usage examples are provided as Jupyter notebooks in the examples directory.

Publications

[0] Graser, A. & Dragaschnig, M. (2025). Learning From Trajectory Data With MobiML. Workshop on Big Mobility Data Analysis (BMDA2025) in conjunction with EDBT/ICDT 2025.

@inproceedings{graser2025learning,
  title={Learning From Trajectory Data With {MobiML}},
  author={Graser, Anita and Dragaschnig, Melitta},
  booktitle={Proceedings of the Workshop on Big Mobility Data Analysis (BMDA2025) in conjunction with EDBT/ICDT},
  year={2025},
  url={https://ceur-ws.org/Vol-3946/BMDA-6.pdf}
}

Acknowledgements

This work was supported in part by the Austrian Federal Ministry for Transport, Innovation and Technology (BMVIT) within the programme ‘AI for Green 2023’ under project No. FO999910218 (AI4PT) as well as by the Horizon Framework Programme of the European Union under grant agreement No. 101070279 (MobiSpaces).