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 Bike Network Planning in Limited Urban Space was accepted for publication in Transportation Research Part B: Methodological! This paper was part of the e-bike city project, a multidisciplinary initiative at the department exploring the effects of a radically changed urban road space with priority on cycling. Planning a suitable bike network is a …
Continue reading “New paper on optimal bike network planning”
A new paper titled “Designing an E-Bike City: An automated process for network-wide multimodal road space reallocation” was published in the Journal of Cycling and Micromobility Research. How can we add transport capacity for rapidly growing urban populations? How can urban transportation systems be transformed for lower emissions within the short time available? In dense …
A new paper titled “Enhancing Deep Learning-Based City-Wide Traffic Prediction Pipelines Through Complexity Analysis” was published in the journal Data Science for Transportation. Our research introduces a novel metric that allows for the pre-modelling complexity evaluation of traffic prediction tasks. This metric, designed for computational efficiency and to be architecture-agnostic, assists in choosing the most …
Continue reading “New Paper on Understanding Complexity of Urban Traffic Prediction”