Real-Time Detection of Traffic From Twitter Stream Analysis - 2015
Social networks have been recently used as a source of information for event detection, with explicit reference to road hold up and automotive accidents. In this paper, we have a tendency to gift a true-time monitoring system for traffic event detection from Twitter stream analysis. The system fetches tweets from Twitter according to several search criteria; processes tweets, by applying text mining techniques; and at last performs the classification of tweets. The aim is to assign the suitable class label to every tweet, as related to a traffic event or not. The traffic detection system was employed for real-time monitoring of several areas of the Italian road network, permitting for detection of traffic events virtually in real time, usually before online traffic news net sites. We have a tendency to used the support vector machine as a classification model, and we have a tendency to achieved an accuracy worth of 95.75percent by solving a binary classification drawback (traffic versus nontraffic tweets). We have a tendency to were also in a position to discriminate if traffic is caused by an external event or not, by solving a multiclass classification drawback and obtaining an accuracy value of 88.eighty nine%.
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