Detección de incidentes de tránsito en Twitter

Nowadays, traffic has become a real chaos in big cities, affecting the mobility of millions of people. On the other hand, social networks handle large amounts of publications dealing divers topics. In particular, many of these publications are shared with the aim of warning about traffic incidents. In this paper, an approach that combines Machine Learning and Natural Language Processing techniques to detect traffic incidents posted on Twitter is proposed. The viability and effectiveness of this approach was evaluated in a study case showing promising results.