This repository provides an approach for reproducing the user’s personal mobility agenda that is able to predict the user’s positions for the whole day. It reproduce the agenda by exploiting a data-driven personal mobility model able to capture and summarize different aspects of the systematic mobility behavior of a user. Baselines are also available.
In this repository we provide the source code of the method and the baselines used in
Riccardo Guidotti, Roberto Trasarti, Mirco Nanni, Fosca Giannotti, Dino Pedreschi "There's A Path For Everyone: A Data-Driven Personal Model Reproducing Mobility Agendas", DSAA 2017, 2017, Tokio, Japan.
Please cite the paper above if you use our code.
Requirements:
- Python >= 2.7.11
- psycopg2
- numpy
- sklearn
- scipy