Information extraction from social media for route planning

Abstract

Micro-blogging is an emerging form of communication and became very popular in recent years. Micro-blogging services allow users to publish updates as short text messages that are broadcast to the followers of users in real-time. Twitter is currently the most popular micro-blogging service. It is a rich and real-time information source and a good way to discover interesting content or to follow recent developments. Additionally, the updates published on Twitter public timeline can be retrieved through their API. A significant amount of traffic information exists on Twitter platform. Twitter users tweet when they are in traffic about accidents, road closures or road construction. With this in mind, this paper presents a system that extracts traffic information from Twitter to be used in route planning. Route planning is of increasing importance as societies try to reduce their energy consumption. Furthermore, route planning is concerned with two types of constraints: stable, such as distance between two points and temporary such as weather conditions, traffic jams or road construction. Our system attempt to extract these temporary constraints from Twitter. We train Naive bayes, Maxent and SVM classifiers to filter non relevant traffic. We then apply NER on traffic tweets to extract locations, highwaysand directions. These extracted locations are then geocoded and used in route planning to avoid routes with traffic jams.