Monitoring and Analysis of Real time detection of traffic from twitter stream analysis
Keywords:
Twitter, Traffic event detection, tweet classification, text mining, social sensingAbstract
Social networks have been, at a recent time utilized as a source of information for event detection, with particular referral to road traffic congestion and for the car accidents. In this paper, we present a real-time monitoring system for traffic event detection from the twitter stream analysis. The system fetches tweets from the twitter according to several search criteria; processes tweets, by applying the text mining techniques; and finally performs the classification of tweets. The aim is to assign the suitable class label to each tweet, as relevant to a traffic event or not. The traffic detection system was exploited for real-time monitoring of several areas of the Italian road network, allowing for detection of traffic events almost in real time, generally before online traffic news web sites. We employed the support vector machine as a classification model, and we attain an accuracy value of 95.75% by solving a binary classification problem (traffic versus non-traffic tweets). We would also able to discriminate if traffic is caused by an external event or not, by solving a multiclass classification issue and obtaining accuracy value of 88.89%.
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