Deep Learning for Human Mobility: a Survey on Data and Models

From Communauté de la Fabrique des Mobilités

Deep Learning for Human Mobility: a Survey on Data and Models

💼 porté par


This document aims to track the progress in the usage of Deep Learning (DL) applied to human mobility and give an overview of the state-of-the-art across the most common tasks and their corresponding datasets. In particular, we want to provide the users with a list of papers and they key characteristics (e.g., DL component(s) used in the model, metric(s) adopted for the evaluation and others) and, whenever it is open, a link to the dataset used in the paper.

Moreover, we provide a list of datasources that can be used to model human mobility (e.g., Call Detail Records, GPS trajectories, Location Based Social Networks) and others that are not representing mobility but are strictly related to it and may be taken into account to finetune predictions (e.g., weather conditions, traffic data and others).

This repository is based on the findings discussed in Deep Learning for Human Mobility: a Survey on Data and Models a paper by Massimiliano Luca, Gianni Barlacchi, Bruno Lepri and Luca Pappalardo.

Organizations using or interested in using the resource:


Tags: deep learning, trace, GPS, dataset, bibliographie

Categories: Connaissance

Theme: Données ouvertes, Traces de mobilité et des données associées


Challenge: Augmenter les connaissances partagées en cartographie et usages des véhicules et réseaux de transports

Key people to solicit:

Other related common: Scikit mobility analysis

Wealth sought: Contributeur - Communauté

Required skills: Information/orientation, Information/simulation

Community of interest: Communauté autour des traces de mobilité et des données associées

License: Creative Commons

Terms of Service (TOS):

Level of development:

Link to my actions board:

Link to my cloud, wiki, drive…:


Next step:

Documentation of the experimentations:

Other informations

List of the actors using or willing of using this common: aucun pour le moment

List of the workshop reports related to this common: