We develop innovative computational methods for the analysis, simulation & prediction of individual mobility, with the goal of making mobility sustainable.

For this, we combine competences and methods from diverse fields such as Geographic Information Science (GISc), Artificial Intelligence (AI), Data Mining, Transportation Modeling, Spatial Cognition, and Learning Analytics. Our interests span from location-based services (LBS), trajectory data analysis, agent-based models and simulation, algorithms and models for spatio-temporal information, to mobile learning visualizations and learning analytics.

In our research we also place great value on reproducibility and transparency of our work. For this purpose we publish our code on the MIE Lab Github page, and for example provide the Trackintel Python package to standardise preprocessing steps of mobility data.

Read more about our research in the following core areas:

Sustainable Mobility

Location-based services to support people in mobility choices, MaaS offers, sustainability assessments

Computational Methods

Spatio-temporal machine learning, analysis, simulation & prediction of human mobility, user profiling and personalization

Mobility & Energy

Vehicle-2-grid strategies, smart charging, impact of drivetrain technologies, spatio-temporal assessments of technology penetration