A Survey of Real-Time Social-Based Traffic Detection

TL;DR

This survey reviews current techniques using text mining and machine learning, particularly SVM classifiers, for real-time traffic event detection from social media streams, highlighting the effectiveness of these methods.

Contribution

It provides a comprehensive overview of state-of-the-art methods in real-time social-based traffic detection and identifies the most effective techniques like text mining and SVM classifiers.

Findings

SVM classifiers achieved 95.75% accuracy

Text mining techniques are effective for traffic event detection

The surveyed methods show promising real-time detection capabilities

Abstract

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).