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

Richard Soria, Luis Caro, Billy Peralta

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojada2019 38th International Conference of the Chilean Computer Science Society, SCCC 2019
EditorialIEEE Computer Society
ISBN (versión digital)9781728156132
DOI
EstadoPublicada - nov 2019
Evento38th International Conference of the Chilean Computer Science Society, SCCC 2019 - Concepcion, Chile
Duración: 4 nov 20199 nov 2019

Serie de la publicación

NombreProceedings - International Conference of the Chilean Computer Science Society, SCCC
Volumen2019-November
ISSN (versión impresa)1522-4902

Conferencia

Conferencia38th International Conference of the Chilean Computer Science Society, SCCC 2019
País/TerritorioChile
CiudadConcepcion
Período4/11/199/11/19

Áreas temáticas de ASJC Scopus

  • Ingeniería (todo)
  • Informática (todo)

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