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Traffic Analysis Based on Short Texts from Social Media

Traffic Analysis Based on Short Texts from Social Media

Ana Maria Magdalena Saldana-Perez, Marco Moreno-Ibarra
Copyright: © 2016 |Volume: 7 |Issue: 1 |Pages: 17
ISSN: 1947-8429|EISSN: 1947-8437|EISBN13: 9781466692527|DOI: 10.4018/IJKSR.2016010105
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MLA

Saldana-Perez, Ana Maria Magdalena, and Marco Moreno-Ibarra. "Traffic Analysis Based on Short Texts from Social Media." IJKSR vol.7, no.1 2016: pp.63-79. http://doi.org/10.4018/IJKSR.2016010105

APA

Saldana-Perez, A. M. & Moreno-Ibarra, M. (2016). Traffic Analysis Based on Short Texts from Social Media. International Journal of Knowledge Society Research (IJKSR), 7(1), 63-79. http://doi.org/10.4018/IJKSR.2016010105

Chicago

Saldana-Perez, Ana Maria Magdalena, and Marco Moreno-Ibarra. "Traffic Analysis Based on Short Texts from Social Media," International Journal of Knowledge Society Research (IJKSR) 7, no.1: 63-79. http://doi.org/10.4018/IJKSR.2016010105

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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.

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