论文标题
实时基于社会的交通检测调查
A Survey of Real-Time Social-Based Traffic Detection
论文作者
论文摘要
在线交通新闻网站并不总是实时宣布各个区域的交通活动。在Twitter流上采用文本挖掘和机器学习技术来执行事件检测，以开发实时的交通检测系统。在本次调查文件中，我们将仔细考虑目前的最新技术，以实时检测交通事件，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分类器可得出最佳结果（即精度为95.75％，F1得分为95.8％）。
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).
