Traffic Analysis Based on Short Texts from Social Media

Abstract

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.

Conceptualizing Big Social Data

“…The sheer volume and semantic richness of such data opens enormous possibilities for utilizing and analyzing it for personal [7,8], commercial [9,10] as well as societal purposes [11][12][13]. For example, the scattered social media would benefit from meta-services that bring together all the content from a user.…”