A deep learning approach for detecting traffic accidents from social media data
A deep learning approach for detecting traffic accidents from social media data
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DOI：
10.1016/j.trc.2017.11.027
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发表时间：
2018-01
期刊：
影响因子：
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通讯作者：
Zhenhua Zhang;Qi He;Jing Gao;Ming Ni
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文献类型：
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作者：
Zhenhua Zhang;Qi He;Jing Gao;Ming Ni
本文采用深度学习从社交媒体数据中检测交通事故。首先，我们彻底调查了北方弗吉尼亚州和纽约市两个大都市地区一年内超过300万条推文内容。我们的研究结果表明，成对的令牌可以捕获的关联规则中的事故相关的推文，进一步提高交通事故检测的准确性。其次，研究了两种深度学习方法：深度信念网络（DBN）和长短期记忆（LSTM），并在提取的令牌上实现。结果表明，DBN可以获得约44个单独的令牌特征和17对令牌特征的总体准确率为85%。DBN的分类结果优于支持向量机（SVM）和监督潜在狄利克雷分配（sLDA）。最后，为了验证这项研究，我们将事故相关的推文与来自15，000个循环检测器的高速公路上的交通事故日志和当地道路上的交通数据进行了比较。研究发现，近66%的事故相关推文可以通过事故日志定位，其中80%以上可以与附近的异常交通数据相关联。通过比较，提出了使用Twitter检测交通事故的几个重要问题，包括位置和时间偏差，以及有影响力的用户和标签的特征。
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.