Mobility Information Engineering Lab at ETH Zürich

trackintel

trackintel is a framework for spatio-temporal analysis of movement trajectory and mobility data.

location-prediction

[TRC] Context-aware next location prediction

location-mode-prediction

[SIGSPATIAL '22] Next location prediction considering travel mode

spatial_rf_python

Benchmarking of spatial regression methods with respect to spatial heterogeneity, and providing a Python implementation of spatial Random Forests

Graph-based-mobility-profiling

Code accompanying our paper "Graph-based Mobility Profiling"

traffic4cast

Submission to the iarai traffic4cast competition

mode_detect

[JTRG] Geospatial context importance for travel mode detection

V2G-carsharing-RL-environment

V2G for car sharing thesis project

bike_lane_optimization

change-detection

[GIScience '21] Travel behaviour change detection study

trip_purpose_privacy

Understanding the predictability of activity purposes

Complexity-Aware-Traffic-Prediction

ev_homepv

A project to analyze how much of our mobility energy demand (induced by electric vehicles) we can cover by using only pv installed on our own home.

geospatial_optimal_transport

graph-trackintel

Tools for building and preprocessing location graphs

reprotrack

rooftop-PV-EV-charging

topology_privacy

How privacy-preserving are graph representations of mobiltiy

train_delay

v2g4carsharing

Vehicle-to-grid strategies for car sharing systems

.github

bike_network_evaluation

car_sharing_simulator

Agent-based car sharing simulator

cognitive_route_planning

energymanager

HackZürich2022 - Energy manager tool

geospatialOT

Optimal transport for evaluating geospatial predictions

predict-visits

teaching_examples

trackintel-tutorial

Tutorial materials for trackintel library

webapp_bike_lane_optimization

Web app for planning bike infrastructure