Automatic tweet generation from traffic incident data

Abstract

We examine the use of traffic information with other knowledge sources to automatically generate natural language tweets similar to those created by humans. We consider how different forms of information can be combined to provide tweets customized to a particular location and/or specific user. Our approach is based on data-driven natural language generation (NLG) techniques using corpora containing examples of natural language tweets. It specifically draws upon semantic data and knowledge developed and used in the web based Connected Vehicles and Smart Transportation system. We introduce an alignment model, generation model and location-based user model which will together support location-relevant information delivery. We provide examples of our system output and discuss evaluation issues with generated tweets.