Cargando…

Trace2trace—A Feasibility Study on Neural Machine Translation Applied to Human Motion Trajectories

Neural machine translation is a prominent field in the computational linguistics domain. By leveraging the recent developments of deep learning, it gave birth to powerful algorithms for translating text from one language to another. This study aims to assess the feasibility of transferring the neura...

Descripción completa

Detalles Bibliográficos
Autores principales: Crivellari, Alessandro, Beinat, Euro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7348925/
https://www.ncbi.nlm.nih.gov/pubmed/32575822
http://dx.doi.org/10.3390/s20123503
_version_ 1783556944675471360
author Crivellari, Alessandro
Beinat, Euro
author_facet Crivellari, Alessandro
Beinat, Euro
author_sort Crivellari, Alessandro
collection PubMed
description Neural machine translation is a prominent field in the computational linguistics domain. By leveraging the recent developments of deep learning, it gave birth to powerful algorithms for translating text from one language to another. This study aims to assess the feasibility of transferring the neural machine translation approach into a completely different context, namely human mobility and trajectory analysis. Building a conceptual parallelism between sentences (sequences of words) and motion traces (sequences of locations), we aspire to translate individual trajectories generated by a certain category of users into the corresponding mobility traces potentially generated by a different category of users. The experiment is inserted in the background of tourist mobility analysis, with the goal of translating the motion behavior of tourists belonging to a specific nationality into the motion behavior of tourists belonging to a different nationality. The model adopted is based on the seq2seq approach and consists of an encoder–decoder architecture based on long short-term memory (LSTM) neural networks and neural embeddings. The encoder turns an input location sequence into a corresponding hidden vector; the decoder reverses the process, turning the vector into an output location sequence. The proposed framework, tested on a real-world large-scale dataset, explores an effective attempt of motion transformation between different entities, arising as a potentially powerful source of mobility information disclosure, especially in the context of crowd management and smart city services.
format Online
Article
Text
id pubmed-7348925
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-73489252020-07-22 Trace2trace—A Feasibility Study on Neural Machine Translation Applied to Human Motion Trajectories Crivellari, Alessandro Beinat, Euro Sensors (Basel) Article Neural machine translation is a prominent field in the computational linguistics domain. By leveraging the recent developments of deep learning, it gave birth to powerful algorithms for translating text from one language to another. This study aims to assess the feasibility of transferring the neural machine translation approach into a completely different context, namely human mobility and trajectory analysis. Building a conceptual parallelism between sentences (sequences of words) and motion traces (sequences of locations), we aspire to translate individual trajectories generated by a certain category of users into the corresponding mobility traces potentially generated by a different category of users. The experiment is inserted in the background of tourist mobility analysis, with the goal of translating the motion behavior of tourists belonging to a specific nationality into the motion behavior of tourists belonging to a different nationality. The model adopted is based on the seq2seq approach and consists of an encoder–decoder architecture based on long short-term memory (LSTM) neural networks and neural embeddings. The encoder turns an input location sequence into a corresponding hidden vector; the decoder reverses the process, turning the vector into an output location sequence. The proposed framework, tested on a real-world large-scale dataset, explores an effective attempt of motion transformation between different entities, arising as a potentially powerful source of mobility information disclosure, especially in the context of crowd management and smart city services. MDPI 2020-06-21 /pmc/articles/PMC7348925/ /pubmed/32575822 http://dx.doi.org/10.3390/s20123503 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Crivellari, Alessandro
Beinat, Euro
Trace2trace—A Feasibility Study on Neural Machine Translation Applied to Human Motion Trajectories
title Trace2trace—A Feasibility Study on Neural Machine Translation Applied to Human Motion Trajectories
title_full Trace2trace—A Feasibility Study on Neural Machine Translation Applied to Human Motion Trajectories
title_fullStr Trace2trace—A Feasibility Study on Neural Machine Translation Applied to Human Motion Trajectories
title_full_unstemmed Trace2trace—A Feasibility Study on Neural Machine Translation Applied to Human Motion Trajectories
title_short Trace2trace—A Feasibility Study on Neural Machine Translation Applied to Human Motion Trajectories
title_sort trace2trace—a feasibility study on neural machine translation applied to human motion trajectories
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7348925/
https://www.ncbi.nlm.nih.gov/pubmed/32575822
http://dx.doi.org/10.3390/s20123503
work_keys_str_mv AT crivellarialessandro trace2traceafeasibilitystudyonneuralmachinetranslationappliedtohumanmotiontrajectories
AT beinateuro trace2traceafeasibilitystudyonneuralmachinetranslationappliedtohumanmotiontrajectories