Learning From Trajectory Data With MobiML

Anita Graser; Melitta Dragaschnig

Learning From Trajectory Data With MobiML

Anita Graser (Autor:in und Vortragende:r)
, Melitta Dragaschnig

Data Science & Artifical Intelligence

Publikation: Beitrag in Buch oder Tagungsband › Vortrag mit Beitrag in Tagungsband › Begutachtung

Abstract

This demo paper introduces MobiML, a new library that aims to help scientists and engineers with developing mobility ML solutions using trajectory data. We also demonstrate how MobiML can speed up ML development workflows using the example of a reproduction of the workflow for training a GeoTrackNet trajectory anomaly detection model. MobiML is available at: https://github.com/movingpandas/mobiml.

Originalsprache	Englisch
Titel	Proceedings of the Workshops of the EDBT/ICDT 2025 Joint Conference
Redakteure/-innen	Matthias Boehm, Khuzaima Daudjee
Seitenumfang	4
Band	3946
Publikationsstatus	Veröffentlicht - 2025
Veranstaltung	7th International Workshop on Big Mobility Data Analytics - co-located with the EDBT/ICDT Conference, Barcelona, Spanien Dauer: 25 März 2025 → 28 März 2025 https://www.datastories.org/bmda25/index.html

Konferenz

Konferenz	7th International Workshop on Big Mobility Data Analytics
Kurztitel	BMDA2025
Land/Gebiet	Spanien
Stadt	Barcelona
Zeitraum	25/03/25 → 28/03/25
Internetadresse	https://www.datastories.org/bmda25/index.html

Research Field

Multimodal Analytics

Zugriff auf Dokument

https://ceur-ws.org/Vol-3946/BMDA-6.pdf

1 Open Source-Software

MobiML: Framework for machine learning from movement data
Graser, A. (Entwickler:in), Dragaschnig, M. (Entwickler:in) & Agarkova, M. (Entwickler:in), 6 März 2025
Publikation: Software › Open Source-Software

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@inproceedings{5b08634c07564f579f9d2107b3168c85,

title = "Learning From Trajectory Data With MobiML",

abstract = "This demo paper introduces MobiML, a new library that aims to help scientists and engineers with developing mobility ML solutions using trajectory data. We also demonstrate how MobiML can speed up ML development workflows using the example of a reproduction of the workflow for training a GeoTrackNet trajectory anomaly detection model. MobiML is available at: https://github.com/movingpandas/mobiml.",

keywords = "Machine learning, GeoAI, Mobility Data Science",

author = "Anita Graser and Melitta Dragaschnig",

year = "2025",

language = "English",

volume = "3946",

editor = "Matthias Boehm and Khuzaima Daudjee",

booktitle = "Proceedings of the Workshops of the EDBT/ICDT 2025 Joint Conference",

note = "7th International Workshop on Big Mobility Data Analytics, BMDA2025 ; Conference date: 25-03-2025 Through 28-03-2025",

url = "https://www.datastories.org/bmda25/index.html",

}

TY - GEN

T1 - Learning From Trajectory Data With MobiML

AU - Dragaschnig, Melitta

A2 - Graser, Anita

A2 - Boehm, Matthias

A2 - Daudjee, Khuzaima

PY - 2025

Y1 - 2025

N2 - This demo paper introduces MobiML, a new library that aims to help scientists and engineers with developing mobility ML solutions using trajectory data. We also demonstrate how MobiML can speed up ML development workflows using the example of a reproduction of the workflow for training a GeoTrackNet trajectory anomaly detection model. MobiML is available at: https://github.com/movingpandas/mobiml.

AB - This demo paper introduces MobiML, a new library that aims to help scientists and engineers with developing mobility ML solutions using trajectory data. We also demonstrate how MobiML can speed up ML development workflows using the example of a reproduction of the workflow for training a GeoTrackNet trajectory anomaly detection model. MobiML is available at: https://github.com/movingpandas/mobiml.

KW - Machine learning

KW - GeoAI

KW - Mobility Data Science

M3 - Conference Proceedings with Oral Presentation

VL - 3946

BT - Proceedings of the Workshops of the EDBT/ICDT 2025 Joint Conference

T2 - 7th International Workshop on Big Mobility Data Analytics

Y2 - 25 March 2025 through 28 March 2025

ER -

Learning From Trajectory Data With MobiML

Abstract

Konferenz

Research Field

Zugriff auf Dokument

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MobiML: Framework for machine learning from movement data

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