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Outlier Vehicle Trajectory Detection Using Deep Autoencoders in Santiago, Chile

In the last decade, a large amount of data from vehicle location sensors has been generated due to the massification of GPS systems to track them. This is because these sensors usually include multiple variables such as position, speed, angular position of the vehicle, etc., and, furthermore, they a...

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Autores principales: Peralta, Billy, Soria, Richard, Nicolis, Orietta, Ruggeri, Fabrizio, Caro, Luis, Bronfman, Andrés
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921668/
https://www.ncbi.nlm.nih.gov/pubmed/36772479
http://dx.doi.org/10.3390/s23031440
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author Peralta, Billy
Soria, Richard
Nicolis, Orietta
Ruggeri, Fabrizio
Caro, Luis
Bronfman, Andrés
author_facet Peralta, Billy
Soria, Richard
Nicolis, Orietta
Ruggeri, Fabrizio
Caro, Luis
Bronfman, Andrés
author_sort Peralta, Billy
collection PubMed
description In the last decade, a large amount of data from vehicle location sensors has been generated due to the massification of GPS systems to track them. This is because these sensors usually include multiple variables such as position, speed, angular position of the vehicle, etc., and, furthermore, they are also usually recorded in very short time intervals. On the other hand, routes are often generated so that they do not correspond to reality, due to artifacts such as buildings, bridges, or sensor failures and where, due to the large amount of data, visual analysis of human expert is unable to detect genuinely anomalous routes. The presence of such abnormalities can lead to faulty sensors being detected which may allow sensor replacement to reliably track the vehicle. However, given the reliability of the available sensors, there are very few examples of such anomalies, which can make it difficult to apply supervised learning techniques. In this work we propose the use of unsupervised deep neural network models based on stacked autoencoders to detect anomalous routes in vehicles within Santiago de Chile. The results show that the proposed model is capable of effectively detecting anomalous paths in real data considering validation given by an expert user, reaching a performance of 82.1% on average. As future work, we propose to incorporate the use of Long Short-Term Memory (LSTM) and attention-based networks in order to improve the detection of anomalous trajectories.
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spelling pubmed-99216682023-02-12 Outlier Vehicle Trajectory Detection Using Deep Autoencoders in Santiago, Chile Peralta, Billy Soria, Richard Nicolis, Orietta Ruggeri, Fabrizio Caro, Luis Bronfman, Andrés Sensors (Basel) Article In the last decade, a large amount of data from vehicle location sensors has been generated due to the massification of GPS systems to track them. This is because these sensors usually include multiple variables such as position, speed, angular position of the vehicle, etc., and, furthermore, they are also usually recorded in very short time intervals. On the other hand, routes are often generated so that they do not correspond to reality, due to artifacts such as buildings, bridges, or sensor failures and where, due to the large amount of data, visual analysis of human expert is unable to detect genuinely anomalous routes. The presence of such abnormalities can lead to faulty sensors being detected which may allow sensor replacement to reliably track the vehicle. However, given the reliability of the available sensors, there are very few examples of such anomalies, which can make it difficult to apply supervised learning techniques. In this work we propose the use of unsupervised deep neural network models based on stacked autoencoders to detect anomalous routes in vehicles within Santiago de Chile. The results show that the proposed model is capable of effectively detecting anomalous paths in real data considering validation given by an expert user, reaching a performance of 82.1% on average. As future work, we propose to incorporate the use of Long Short-Term Memory (LSTM) and attention-based networks in order to improve the detection of anomalous trajectories. MDPI 2023-01-28 /pmc/articles/PMC9921668/ /pubmed/36772479 http://dx.doi.org/10.3390/s23031440 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Peralta, Billy
Soria, Richard
Nicolis, Orietta
Ruggeri, Fabrizio
Caro, Luis
Bronfman, Andrés
Outlier Vehicle Trajectory Detection Using Deep Autoencoders in Santiago, Chile
title Outlier Vehicle Trajectory Detection Using Deep Autoencoders in Santiago, Chile
title_full Outlier Vehicle Trajectory Detection Using Deep Autoencoders in Santiago, Chile
title_fullStr Outlier Vehicle Trajectory Detection Using Deep Autoencoders in Santiago, Chile
title_full_unstemmed Outlier Vehicle Trajectory Detection Using Deep Autoencoders in Santiago, Chile
title_short Outlier Vehicle Trajectory Detection Using Deep Autoencoders in Santiago, Chile
title_sort outlier vehicle trajectory detection using deep autoencoders in santiago, chile
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921668/
https://www.ncbi.nlm.nih.gov/pubmed/36772479
http://dx.doi.org/10.3390/s23031440
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