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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).

Research

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

News

Ye Hong presented at STRC 2024

Ye Hong gave a talk at the Swiss Transport Research Conference (STRC) 2024 with the title Towards realistic individual activity location demand synthesis using deep generative networks. Reach out if you are interested in the topic and want to learn more!

New Paper in Transportation Research Part C: Quantifying the Dynamic Predictability of Train Delays

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 …