A Survey of Real-Time Social-Based Traffic Detection
It addresses the problem of real-time traffic detection for users relying on online news, but is incremental as it surveys and compares existing methods.
This survey paper reviews existing techniques for real-time traffic event detection using text mining and machine learning on Twitter data, highlighting that applying text mining with SVM classifiers achieved the best results of 95.75% accuracy and 95.8% F1-score.
Online traffic news web sites do not always announce traffic events in areas in real-time. There is a capability to employ text mining and machine learning techniques on the twitter stream to perform event detection, in order to develop a real-time traffic detection system. In this present survey paper, we will deliberate the current state-of-art techniques in detecting traffic events in real-time focusing on five papers [1, 2, 3, 4, 5]. Lastly, applying text mining techniques and SVM classifiers in paper [2] gave the best results (i.e. 95.75% accuracy and 95.8% F1-score).