Deep Learning for Human Mobility: a Survey on Data and Models
Deep Learning for Human Mobility: a Survey on Data and Models
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.
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Tags: deep learning, trace, GPS, dataset, bibliographie
Categories: Connaissance
Theme: Données ouvertes, Traces de mobilité et des données associées
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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
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