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

https://github.com/scikit-mobility/DeepLearning4HumanMobility

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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.

Organisations utilisatrices ou intéressées pour utiliser la ressource :

Contributeur(s) :

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

Catégories : Connaissance

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

Référent :

Défi auquel répond la ressource : Augmenter les connaissances partagées en cartographie et usages des véhicules et réseaux de transports

Personnes clés à solliciter :

Autre commun proche : Scikit mobility analysis

Richesse recherchée : Contributeur - Communauté

Compétences recherchées : Information/orientation, Information/simulation

Communauté d'intérêt : Communauté autour des traces de mobilité et des données associées