A deep learning approach for detecting traffic accidents from social media data

TL;DR

This study leverages deep learning on social media data, specifically tweets, to detect traffic accidents with high accuracy, outperforming traditional methods and validated against traffic logs and sensor data.

Contribution

It introduces a novel approach combining paired token analysis and deep learning models like DBN and LSTM for traffic accident detection from social media.

Findings

DBN achieved 85% accuracy with token features

66% of accident-related tweets matched traffic logs

Over 80% of tweets linked to abnormal traffic data

Abstract

This paper employs deep learning in detecting the traffic accident from social media data. First, we thoroughly investigate the 1-year over 3 million tweet contents in two metropolitan areas: Northern Virginia and New York City. Our results show that paired tokens can capture the association rules inherent in the accident-related tweets and further increase the accuracy of the traffic accident detection. Second, two deep learning methods: Deep Belief Network (DBN) and Long Short-Term Memory (LSTM) are investigated and implemented on the extracted token. Results show that DBN can obtain an overall accuracy of 85% with about 44 individual token features and 17 paired token features. The classification results from DBN outperform those of Support Vector Machines (SVMs) and supervised Latent Dirichlet allocation (sLDA). Finally, to validate this study, we compare the accident-related tweets with both the traffic accident log on freeways and traffic data on local roads from 15,000 loop detectors. It is found that nearly 66% of the accident-related tweets can be located by the accident log and more than 80% of them can be tied to nearby abnormal traffic data. Several important issues of using Twitter to detect traffic accidents have been brought up by the comparison including the location and time bias, as well as the characteristics of influential users and hashtags.