2020
Master thesis  Unknown

Generative Models of Human Mobility based on Deep Learning

Briganti S.

data science  human mobility  mobility data  mobility analysis  generative models  artificial intelligence  machine learning 

Goal of the thesis is the generation of synthetic human mobility based on Deep Learning. Three different generative recurrent models have been implemented: a Seq2Seq Variational Autoencoder (VAE), a Generative Adversarial Network (GAN) and a Wasserstein GAN. The aim of this study is the generation of a synthetic dataset of GPS trajectories having characteristics and typical measures proper of the real human mobility. Scopo della tesi è la generazione di mobilità umana sintetica basata suDeep Learning. Sono stati implementati tre modelli generativi: un Seq2Seq Variational Autoencoder (VAE), una Generative Adversarial Network (GAN) e una Wasserstein GAN. Obiettivo finale dello studio è lagenerazione di un dataset sintetico di traiettorie GPS, avente caratteristiche e misure proprie della mobilità umana.



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BibTeX entry
@mastersthesis{oai:it.cnr:prodotti:425769,
	title = {Generative Models of Human Mobility based on Deep Learning},
	author = {Briganti S.},
	year = {2020}
}
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