In 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.

In recent years, the pollution problem has gained great importance due to its socioeconomic implications for people regarding health or logistic issues. The pollution level classically is measured with specialized expensive detectors located in some few locations. In the case of Temuco city there are three such centralized pollution monitoring stations. An alternative approach for measuring the pollution level of cities makes use of inexpensive pollution sensors located on public transportation vehicles. Nonetheless, a drawback of this approach is that these inexpensive sensors can be sensitive to noise, vehicle movement, human intervention or technical failures. Therefore, it is relevant to be able to automatically detect inaccurate or failing sensors as they are multiple and cannot be submitted frequently to a technical revision. In this work, we propose a method to automatically detect these anomalous sensors by an unsupervised deep learning approach using autoencoders. This work is part of an ongoing project where massive data are not still available. In this context, the simulated output of mobile pollution sensors is generated by a time series model that systematically inserts outlier measurements. Our results indicate that the proposed detection method is able to reliably reproduce the data generated and to detect the simulated outliers with an accuracy of over 95%. A post-publication change was made to this article on 3 Jul 2020 to correct an author name.

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