Abstract
Internet sites are sources of information for the detection of events, a special mention of traffic activity and accidental accidents or earthquake detection system. Because of the rapid growth of the last 20 years, there have been frequent traffic congestions in cities around the world. The increase in vehicles has caused a greater number of traffic events and, as a result, there are no common resources. We present a methodology for the acquisition, processing and classification of public Tweets with Natural Language Processing (NLP) techniques using the Vector Machine Support (SVM) algorithm, using text classification using social network data to detect incidents. Our view can detect tweets related to traffic, with an accuracy of 88.27%. In this document, we focus on a real-time monitoring system to detect traffic, for Twitter streams analysis by ranking of Twitter posts. We cannot even distinguish if an outdoor event throws traffic or not, multiplying the classification problem and correcting it by point 88.89%.
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Afzaal, M. et al. (2019). Real Time Traffic Incident Detection by Using Twitter Stream Analysis. In: Ahram, T., Karwowski, W., Taiar, R. (eds) Human Systems Engineering and Design. IHSED 2018. Advances in Intelligent Systems and Computing, vol 876. Springer, Cham. https://doi.org/10.1007/978-3-030-02053-8_95
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DOI: https://doi.org/10.1007/978-3-030-02053-8_95
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