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Current social networks provide information with high correlation with events that are occurring worldwide. Twitter is a microblogging network of real time messages in which people post about various classes of events. A relevant topic is traffic congestion; user-generated content is useful to assist drivers in avoiding crowded areas. This work proposes a model to predict traffic-related events, based on a set of machine learning methods, in which a spatio-temporal dataset is obtained from Twitter messages. The training stage uses geocoded traffic events, in order to generate possible sites with traffic congestion at a given time. As a case study, partial areas of the Mexico City were taking into consideration. [ABSTRACT FROM AUTHOR]