Real-Time Detection of Traffic From Twitter Stream Analysis

Internet sites are source of info for event detection, with specific mention of the road traffic activity blockage and accidents or earth-quack sensing system. In this paper, we present a real-time monitoring system intended for traffic occasion detection coming from Twitter stream analysis. The system fetches tweets coming from Twitter as per a several search criteria; methods tweets, by applying textual content mining methods; last but not least works the classification of twitter posts. The goal is to assign suitable class packaging to every tweet, because related with an activity of traffic event or perhaps not. The traffic recognition system or framework was utilized for real- time monitoring of various areas of the street network, taking into account detection of traffic occasions just almost in actual time, regularly before on-line traffic news sites. All of us employed the support vector machine like a classification unit, furthermore, we accomplished a great accuracy value of ninety five. 75% by attempting a binary classification issue. All of us were also capable to discriminate if traffic is triggered by an external celebration or not, by resolving a multiclass classification issue and obtaining accuracy worth of 88. 89%. Key Words: Twitter, Social media; Traffic detection; Text mining; Privacy; Service Oriented Architecture (SOA), machine learning, Twitter stream analysis, Traffic event detection, tweet classification, text mining, social sensing. Keywords: Twitter-Traffic-Status Language: en