Outlier Detection on Vehicle Trajectories in Santiago, Chile using Unsupervised Deep Learning

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datacite.alternateIdentifier.citation2019 38TH INTERNATIONAL CONFERENCE OF THE CHILEAN COMPUTER SCIENCE SOCIETY (SCCC),Vol.,,2019
datacite.creatorSoria, Richard
datacite.creatorCaro Saldivia, Luis
datacite.creatorPeralta Márquez, Billy
datacite.creatorIEEE
datacite.date2019
datacite.subject.englishOutlier Detection
datacite.subject.englishVehicle Trajectory
datacite.subject.englishDeep Learning
datacite.titleOutlier Detection on Vehicle Trajectories in Santiago, Chile using Unsupervised Deep Learning
dc.date.accessioned2021-04-30T16:32:56Z
dc.date.available2021-04-30T16:32:56Z
dc.description.abstractCurrently, a large amount of data is generated in the telemetry sector of vehicles in cities due to the continuous monitoring of vehicle trajectories through multiple sensors. Some trajectories generated by the sensors turn out not to correspond to the reality due to artefacts such as buildings, bridges or sensor failures, and where due to their large volume a manual verification of their correctness is not feasible. In this work, we propose the use of deep neural network models without supervision based on stacked autoencoders to detect atypical trajectories in vehicles within Santiago, Chile. The results show that the proposed model shows that it is able to detect that the atypical vehicle paths detected are at least 85% correct when considering the validation of a human expert. As future work, we propose to incorporate the use of LSTM networks in our model.
dc.identifier.urihttp://repositoriodigital.uct.cl/handle/10925/2936
dc.language.isoen
dc.publisherIEEE
dc.source2019 38TH INTERNATIONAL CONFERENCE OF THE CHILEAN COMPUTER SCIENCE SOCIETY (SCCC)
oaire.resourceTypeMeeting
uct.catalogadorWOS
uct.indizacionISTP
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