The framework was evaluated by comparing updates generated to observations made at 10 selected locations within the Lagos metropolis. Machine learning tech-niques were also used to identify tweets that were related to traffic, reported traffic and also the intensity of traffic reported. The system utilized Natural Language Processing (NLP), Text Mining and a Knowledge Discovery (KDD) and Data Mining process to identify physical locations mentioned in a tweet text (geo-matching). This characteristic of tweets has been used to extract real-time geo-spatial information such as incident and crime reports from Twitter feeds This project developed a framework for generating accurate real-time vehicular traffic updates about the city of Lagos, Nigeria, from Twitter feeds. The short 140 character format of Twitter messages combined with its usage on mobile phones make Twitter ideal for sending messages about the user’s current situation and/or location. Microblogging platform Twitter, is one such network. Online social networks have become a global phenomenon in recent years.
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