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

Richard Soria, Luis Caro, Billy Peralta

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Currently, 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.

Original languageEnglish
Title of host publication2019 38th International Conference of the Chilean Computer Science Society, SCCC 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728156132
DOIs
Publication statusPublished - Nov 2019
Event38th International Conference of the Chilean Computer Science Society, SCCC 2019 - Concepcion, Chile
Duration: 4 Nov 20199 Nov 2019

Publication series

NameProceedings - International Conference of the Chilean Computer Science Society, SCCC
Volume2019-November
ISSN (Print)1522-4902

Conference

Conference38th International Conference of the Chilean Computer Science Society, SCCC 2019
Country/TerritoryChile
CityConcepcion
Period4/11/199/11/19

Keywords

  • Deep Learning
  • Outlier Detection
  • Vehicle Trajectory

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science(all)

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