Traffic observatory: a system to detect and locate traffic events and conditions using Twitter

Abstract

Twitter has become one of the most popular platforms for sharing user-generated content, which varies from ordinary conversations to information about recent events. Studies have already showed that the content of tweets has a high degree of correlation with what is going on in the real world. A type of event which is commonly talked about in Twitter is traffic. Aiming to help other drivers, many users tweet about current traffic conditions, and there are even user accounts specialized on the subject. With this in mind, this paper proposes a method to identify traffic events and conditions in Twitter, geocode them, and display them on the Web in real time. Preliminary results showed that the method is able to detect neighborhoods and thoroughfares with a precision that varies from 50 to 90%, depending on the number of places mentioned in the tweets.