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Recently, researchers have shown an increased interest in harnessing Twitterdata for dynamic monitoring of traffic conditions. Bag-of-words representationis a common method in literature for tweet modeling and retrieving trafficinformation, yet it suffers from the curse of dimensionality and sparsity. Toaddress these issues, our specific objective is to propose a simple and robustframework on the top of word embedding for distinguishing traffic-relatedtweets against non-traffic-related ones. In our proposed model, a tweet isclassified as traffic-related if semantic similarity between its words and asmall set of traffic keywords exceeds a threshold value. Semantic similaritybetween words is captured by means of word-embedding models, which is anunsupervised learning tool. The proposed model is as simple as having only onetrainable parameter. The model takes advantage of outstanding merits, which aredemonstrated through several evaluation steps. The state-of-the-art testaccuracy for our proposed model is 95.9%.

Twitter-based traffic information system based on vector representations for words