  Recently, researchers have shown an increased interest in harnessing Twitter
data for dynamic monitoring of traffic conditions. Bag-of-words representation
is a common method in literature for tweet modeling and retrieving traffic
information, yet it suffers from the curse of dimensionality and sparsity. To
address these issues, our specific objective is to propose a simple and robust
framework on the top of word embedding for distinguishing traffic-related
tweets against non-traffic-related ones. In our proposed model, a tweet is
classified as traffic-related if semantic similarity between its words and a
small set of traffic keywords exceeds a threshold value. Semantic similarity
between words is captured by means of word-embedding models, which is an
unsupervised learning tool. The proposed model is as simple as having only one
trainable parameter. The model takes advantage of outstanding merits, which are
demonstrated through several evaluation steps. The state-of-the-art test
accuracy for our proposed model is 95.9%.
