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 related to traffic accidents 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 in extracted tokens. 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.
Zhang et al. REF used two methods of deep learning: deep belief network (DBN) and long-term short-term memory (LSTM) to study the extracted tokens which can capture the association rules inherent in the accident.
A deep learning approach for detecting traffic accidents from social media data
Zhang, Z., He, Q., Gao, J. and Ni, M., 2018. A deep learning approach for detecting traffic accidents from social media data. Transportation Research Part C: Emerging Technologies, 86, pp.580-596
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