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).

中文翻译：

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基于社交的实时交通检测调查

在线交通新闻网站并不总是实时发布区域内的交通事件。有能力在 Twitter 流上使用文本挖掘和机器学习技术来执行事件检测，以开发实时流量检测系统。在本调查论文中，我们将讨论当前实时检测交通事件的最新技术，重点关注五篇论文 [1, 2, 3, 4, 5]。最后，在论文 [2] 中应用文本挖掘技术和 SVM 分类器给出了最好的结果（即 95.75% 的准确率和 95.8% 的 F1-score）。