Social networks provide information about activities of humans and social events. Thus, with the help of social networks, we can extract the traffic events that occur in a city. In the context of an urban area, this kind of data allows to obtaining contextual real-time information shared among citizens that will be useful to address social, environmental and economic issues. In this paper, the authors describe a methodology to obtain information related to traffic events such as accidents or congestion, from Twitter messages and RSS services. A text mining process is applied on the messages to acquire the relevant data, then data are classified by using a machine learning algorithm. The events are geocoded and transformed into geometric points to be represented on a map. The final repository lets data to be available for further works related to the traffic events on the study area. As a case of study we consider Mexico City.

Social networks provide information about activities of humans and social events. Thus, with the help of social networks, we can extract the traffic events that occur in a city. In the context of an urban area, this kind of data allows to obtaining contextual real-time information shared among citizens that will be useful to address social, environmental and economic issues. In this paper, the authors describe a methodology to obtain information related to traffic events such as accidents or congestion, from Twitter messages and RSS services. A text mining process is applied on the messages to acquire the relevant data, then data are classified by using a machine learning algorithm. The events are geocoded and transformed into geometric points to be represented on a map. The final repository lets data to be available for further works related to the traffic events on the study area. As a case of study we consider Mexico City.

ABSTRACT:The Volunteer Geographic Information (VGI) can be used to understand the urban dynamics. In the classification of traffic related short texts to analyze road problems in urban areas, a VGI data analysis is done over a social media's publications, in order to classify traffic events at big cities that modify the movement of vehicles and people through the roads, such as car accidents, traffic and closures. The classification of traffic events described in short texts is done by applying a supervised machine learning algorithm. In the approach users are considered as sensors which describe their surroundings and provide their geographic position at the social network. The posts are treated by a text mining process and classified into five groups. Finally, the classified events are grouped in a data corpus and geo-visualized in the study area, to detect the places with more vehicular problems.

It is interesting to exploit the user-generated content (UGC) and to use it with a view to infer new data; volunteered geographic information (VGI) is a concept derived from UGC, whose main importance lies in its continuously updated data. The present approach tries to explode the use of VGI by collecting data from a social network and a RSS service; the short texts collected from the social network are written in Spanish language; text mining and a recovery information processes are applied over the data in order to remove special characters on text and to extract relevant information about the traffic events on the study area; then data are geocoded. The texts are classified by using a machine learning algorithm into five classes, each of them represents a specific traffic event or situation.

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