A collection of methods (relying on the scikit-mobility and OSMnx Python libraries) to compute emissions starting from vehicles' mobility trajectories. In particular, methods...

This method provides a Privacy Risk Assessment on mobility data, in terms of trajectories or aggregation of trajectories, i.e., locations with frequency of visit and locations...

We used the mobility-emission methods (https://data.d4science.org/ctlg/ResourceCatalogue/mobility-emissions) to perform experiments on real GPS trajectories describing 433,272...

In this experiment we simulate the mobility of individuals in New York City, London, and Tokyo, using STS-EPR. For each city, we compare the synthetic trajectories generated...

Soccer & Data Cup at Expo Dubai 2020 will be a 3-days international hybrid marathon of Sport Analytics combining fundamental techniques of data analysis and Artificial...

The aim of this study was to investigate whether volleyball-specific skills, physical performance, and general cognitive functions differ between players of different...

Scikit-mobility is a library for human mobility analysis in Python. The library allows to: represent trajectories and mobility flows with proper data structures, TrajDataFrame...

The data module of scikit-mobility provides users with an easy way to: 1) Download ready-to-use mobility data (e.g., trajectories, flows, spatial tessellations, and auxiliary...