mie-lab/geospatial_optimal_transport

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Python
License
MIT License
Created over 1 year ago
Updated 9 months ago

mie-lab/geospatial_optimal_transport

mie-lab

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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 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

Jupyter Notebook

Graph-based-mobility-profiling

Code accompanying our paper "Graph-based Mobility Profiling"

traffic4cast

Submission to the iarai traffic4cast competition

traffic4cast-Graph-ResNet

V2G-carsharing-RL-environment

V2G for car sharing thesis project

Jupyter Notebook

mode_detect

[JTRG] Geospatial context importance for travel mode detection

Jupyter Notebook

trip_purpose_privacy

Understanding the predictability of activity purposes

bike_lane_optimization

change-detection

[GIScience '21] Travel behaviour change detection study

reprotrack

rooftop-PV-EV-charging

trackintel-docs

The trackintel documentation, please visit:

Complexity-Aware-Traffic-Prediction

topology_privacy

How privacy-preserving are graph representations of mobiltiy

train_delay

graph-trackintel

Tools for building and preprocessing location graphs

v2g4carsharing

Vehicle-to-grid strategies for car sharing systems

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.