Publications

This book, co-authored by Mahmoud Sakr , Alejandro Vaisman , Esteban Zimányi covers the key topics in mobility data analysis, with all steps of the pipeline illustrated by real-world examples.

This demo paper introduces MobiML, a new library that aims to help scientists and engineers with developing mobility ML solutions using trajectory data.

This paper addresses short-term Collision-Risk-Aware ship route planning while utilizing a deep learning-based Vessel Collision Risk Assessment and Forecasting (VCRA/F) framework to quantify risks.

Federated Learning for Mobility Applications.  MobiSpaces mentioned in acknowledgements. Relevant work for mobility AI & data spaces

The rapidly evolving field of mobility data spaces, integral to the contemporary technological landscape, creates unique challenges and opportunities in the context of legal compliance.

This paper introduces M³fed, a novel solution for federated learning of movement anomaly detection models. This innovation has the potential to improve data privacy and reduce communication costs in machine learning for movement anomaly detection.

"Developments in Mobilty Data Science", including recent developments in MobiSpaces

In recent years, the maritime domain has experienced tremendous growth due to the exploitation of big traffic data. Particular emphasis has been placed on deep learning methodologies for decision-making.

Trajectory data combines the complexities of time series, spatial data, and (sometimes irrational) movement behavior. As data availability and computing power have increased, so has the popularity of deep learning from trajectory data.

Effective data governance and management are necessary but challenging prerequisites for creating value from data assets. Findability, accessibility, interoperability, and reusability are guiding principles for data owners in managing and archiving datasets, known as the FAIR Principles.