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trackintel
trackintel is a framework for spatio-temporal analysis of movement trajectory and mobility data.location-prediction
[TRC] Context-aware next location predictionlocation-mode-prediction
[SIGSPATIAL '22] Next location prediction considering travel modespatial_rf_python
Benchmarking of spatial regression methods with respect to spatial heterogeneity, and providing a Python implementation of spatial Random ForestsGraph-based-mobility-profiling
Code accompanying our paper "Graph-based Mobility Profiling"traffic4cast
Submission to the iarai traffic4cast competitiontraffic4cast-Graph-ResNet
V2G-carsharing-RL-environment
V2G for car sharing thesis projectmode_detect
[JTRG] Geospatial context importance for travel mode detectiontrip_purpose_privacy
Understanding the predictability of activity purposesbike_lane_optimization
change-detection
[GIScience '21] Travel behaviour change detection studyreprotrack
rooftop-PV-EV-charging
trackintel-docs
The trackintel documentation, please visit:Complexity-Aware-Traffic-Prediction
topology_privacy
How privacy-preserving are graph representations of mobiltiytrain_delay
graph-trackintel
Tools for building and preprocessing location graphsev_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
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