An investigation on optimizing traffic flow based on Twitter Data Analysis

In this paper, we propose a solution to optimize traffic control by considering the earlier traffic analysis methodologies and social data. Traffic control system is a worldwide problem and the social networking sites can be plugged as a source of information for event identification, such as road traffic congestion and car accidents. Big amount of social data will increase in the upcoming years as the usage of portable electronic devices and Internet begin serving a larger population. Here with the availability of the driver's location data from portable and smart devices and feeds from micro-blogging sites such as Facebook and Twitter play a vital role in discovering a proper solution to traffic control in major noted cities. Social webs authorize population to achieve an integrity and give them participate it to produce a neighborhood. We produce a substantive monitoring blueprint for communication experience credit from the report of Twitter pour. The arrangement was designed from strand as occasion-driven root, lean on employment oriented composition and obtains chirps from Twitter planted on discrete search criteria being processes twitters, by letter of text digging methods; and performs Tweet regulation. To detect a target event, tweets have to be classified and clustered based on features like keywords in a tweet, structure of tweet and their context. Users are using Twitter to report real-life events. It focuses on identifying those events by analyzing this text stream in Twitter. The traffic detection system was inked for realtime monitoring of many areas of the road network, that allow for detection of traffic events almost in real time.