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Non-recurrent and unpredictable traffic events directly influence roadtraffic conditions. There is a need for dynamic monitoring and prediction ofthese unpredictable events to improve road network management. The problem withthe existing traditional methods (flow or speed studies) is that the coverageof many Indian roads is very sparse and reproducible methods to identify anddescribe the events are not available. Addition of some other form of data isessential to help with this problem. This could be real-time speed monitoringdata like Google Maps, Waze, etc. or social data like Twitter, Facebook, etc.In this paper, an unsupervised learning model is used to perform effectivetweet classification for enhancing Indian traffic data. The model usesword-embeddings to calculate semantic similarity and achieves a test score of94.7%.

Traffic event description based on Twitter data using Unsupervised Learning Methods for Indian road conditions