  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.
