Mobility Information Engineering Lab at ETH Zürich
Welcome to the Mobility Information Engineering Lab (MIE) at ETH Zürich. The MIE Lab is part of the Chair of Geoinformation Engineering at the Institute of Cartography and Geoinformation (IKG).
Our research is centered around analyzing spatio-temporal aspects of human mobility and developing methods to increase its sustainability with information and communication technology (ICT).
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:
Location-based services to support people in mobility choices, MaaS offers, sustainability assessments
Spatio-temporal machine learning, analysis, simulation & prediction of human mobility, user profiling and personalization
Vehicle-2-grid strategies, smart charging, impact of drivetrain technologies, spatio-temporal assessments of technology penetration
Our paper “A Multiscale Interpretability Framework for Identifying Actionable Road Network Features to Mitigate Congestion in Highly Congested Cities” has been published in Scientific Reports! The paper, led by Nishant Kumar together with Yatao Zhang, Nina Wiedemann, Jimi Oke and Martin Raubal, presents a multiscale framework for identifying road network features associated with traffic congestion …
“
Our full paper “Mapping Cycling Experience in Valence–Arousal Space: A Geometric Approach to Appraisal Heterogeneity” was presented at the 17th International Conference on Spatial Information Theory (COSIT 2026)! The paper, led by Ayda Grisiute together with Johanna Wörle, Panagiotis Mavros (Télécom Paris) and Martin Raubal, introduces a visual-geometric approach to mapping cycling experience. We show …
We welcome Juliette Sikking who will start her PhD at the MIE Lab on the topic of GeoAI and spatial machine learning for human mobility and geospatial data. Juliette is interested in the interplay between machine learning methods and geospatial patterns in urban systems. She is excited about applying spatial machine learning to improve our …