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 on “Quantifying the dynamic predictability of train delay with uncertainty-aware neural networks” has been published in Transportation Research Part C! In light of the importance of accurate delay prediction for transport services and passengers, many predictive methods have been proposed. However, they hardly account for the involved uncertainty and there is a lack …
Location data is extremely sensitive, revealing where we spend how much time, what we like to do in our free time, and how we spend our money. Our new study titled “Where you go is who you are – A study on machine learning based semantic privacy attacks” sets out to answer a critical question: …
Continue reading “New paper in the Journal of Big Data: Where you go is who you are”
Our paper titled “Spatially-aware station based car-sharing demand prediction” is now published open-access in the Journal of Transport Geography! In this paper, we analyze long-term station-based car-sharing demand (i.e., the monthly number of reservations per station), and fit local and global models to the demand. Spatially-aware models and methods for interpretability improved our understanding of …