Outlier Detection on Vehicle Trajectories in Santiago, Chile using Unsupervised Deep Learning
- Soria, Richard - Caro Saldivia, Luis - Peralta Márquez, Billy - IEEE
- Datos de publicación:
- 2019 38TH INTERNATIONAL CONFERENCE OF THE CHILEAN COMPUTER SCIENCE SOCIETY (SCCC),Vol.,,2019
- Outlier Detection - Vehicle Trajectory - Deep Learning
- Migración Web of Science 
- 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.