Outlier Vehicle Trajectory Detection Using Deep Autoencoders in Santiago, Chile

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dc.contributor.authorPeralta, Billy
dc.contributor.authorSoria, Richard
dc.contributor.authorNicolis, Orietta
dc.contributor.authorRuggeri, Fabrizio
dc.contributor.authorCaro, Luis
dc.contributor.authorBronfman, Andrés
dc.date.accessioned2023-08-16T20:11:12Z
dc.date.available2023-08-16T20:11:12Z
dc.date.issued2023-02
dc.descriptionIndexación: Scopuses
dc.description.abstractIn 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. © 2023 by the authors.es
dc.description.urihttps://www.mdpi.com/1424-8220/23/3/1440
dc.identifier.citationSensors Volume 23, Issue 3February 2023 Article number 1440es
dc.identifier.doi10.3390/s23031440
dc.identifier.issn1424-8220
dc.identifier.urihttps://repositorio.unab.cl/xmlui/handle/ria/52568
dc.language.isoenes
dc.publisherMDPIes
dc.rights.licenseAtribución 4.0 Internacional (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.subjectDeep learninges
dc.subjectGPSes
dc.subjectOutlier detectiones
dc.subjectVehicle trajectoryes
dc.titleOutlier Vehicle Trajectory Detection Using Deep Autoencoders in Santiago, Chilees
dc.typeArtículoes
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