Vehicular traffic analysis from social media data

In this paper, we address the problem of vehicular traffic congestion occurring in densely populated cities. Towards this we propose to provide a framework for optimal vehicular traffic solution using social media live data. Typically, the traffic congestion problem addressed in literature focuses on usage of dedicated traffic sensors and satellite information which is quite expensive. However, many urban commuters tend to post updates about traffic on various social media in the form of tweets or Facebook posts. With the copious amount of data made available upon traffic problems on social media sites, we collect historical data about traffic posts from specific cities and build a sentiment classifier to monitor commuters' emotions round the clock. The knowledge is used to analyze and predict traffic patterns in a given location. Also we identify the probable cause of a traffic congestion in a particular area by analyzing the collected historical data. Through our work, we are able to present an uncensored, economical and alternative approach to traditional methods for monitoring traffic congestion.