Early Anomalous Vehicular Traffic Detection Through Spectral Techniques and Unsupervised Learning Models

Smart Mobility seeks to meet urban requirements within a city and solve the urban mobility problems, one of them is related with vehicular traffic. The anomalous vehicular traffic is an unexpected change in the day-to-day vehicular traffic caused by different reasons, such as an accident, an event, road works or a natural disaster. An early detection of anomalous vehicular traffic allows to alert drivers of the anomaly and can make better decisions during their journey. The current solutions for this problem are mainly focused on the development of new algorithms, without giving enough importance to the extraction of underlying information from vehicular traffic, and even more, when this is a univariate time series and it is not possible to obtain other context features that describes its behavior. To address this issue, we propose a methodology for temporary, spectral and aggregation features and an unsupervised learning model to detect anomalous vehicular traffic. The methodology was evaluated in a real vehicular traffic database. Experimental results show that by using spectral attributes the detection of anomalous vehicular traffic, the Isolation Forest obtains the best results.