Department of Software, Sejong University, Seoul, South Korea.
Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan.
Accid Anal Prev. 2021 Mar;151:105973. doi: 10.1016/j.aap.2021.105973. Epub 2021 Jan 15.
Accurate detection of traffic accidents as well as condition analysis are essential to effectively restoring traffic flow and reducing serious injuries and fatalities. This goal can be obtained using an advanced data classification model with a rich source of traffic information. Several systems based on sensors and social networking platforms have been presented recently to detect traffic events and monitor traffic conditions. However, sensor-based systems provide limited information, and may fail owing to the long detection times and high false-alarm rates. In addition, social networking data are unstructured, unpredictable, and contain idioms, jargon, and dynamic topics. The machine learning algorithms utilized for traffic event detection might not extract valuable information from social networking data. In this paper, a social network-based, real-time monitoring framework is proposed for traffic accident detection and condition analysis using ontology and latent Dirichlet allocation (OLDA) and bidirectional long short-term memory (Bi-LSTM). First, the query-based search engine effectively collects traffic information from social networks, and the data preprocessing module transforms it into structured form. Second, the proposed OLDA-based topic modeling method automatically labels each sentence (e.g., traffic or non-traffic) to identify the exact traffic information. In addition, the ontology-based event recognition approach detects traffic events from traffic-related data. Next, the sentiment analysis technique identifies the polarity of traffic events employing user's opinions, which helps determine accurate conditions of traffic events. Finally, the FastText model and Bi-LSTM with softmax regression are trained for traffic event detection and condition analysis. The proposed framework is evaluated using traffic-related data, comparing OLDA and Bi-LSTM with existing topic modeling methods and traditional classifiers using word embedding models, respectively. Our system outperforms state-of-the-art methods and achieves accuracy of 97 %. This finding demonstrates that the proposed system is more efficient for traffic event detection and condition analysis, in comparison to other existing systems.
准确检测交通事故并进行状态分析对于有效恢复交通流量、减少严重伤害和死亡至关重要。这一目标可以通过使用具有丰富交通信息源的先进数据分类模型来实现。最近已经提出了几种基于传感器和社交网络平台的系统来检测交通事件和监测交通状况。然而，基于传感器的系统提供的信息有限，并且可能由于检测时间长和误报率高而失败。此外，社交网络数据是非结构化的、不可预测的，并且包含习语、行话和动态主题。用于交通事件检测的机器学习算法可能无法从社交网络数据中提取有价值的信息。在本文中，提出了一种基于社交网络的实时监测框架，用于使用本体论和潜在狄利克雷分配（OLDA）和双向长短期记忆（Bi-LSTM）进行交通事故检测和状态分析。首先，基于查询的搜索引擎有效地从社交网络中收集交通信息，并且数据预处理模块将其转换为结构化形式。其次，所提出的基于 OLDA 的主题建模方法自动对每个句子（例如交通或非交通）进行标记，以识别准确的交通信息。此外，基于本体论的事件识别方法从与交通相关的数据中检测交通事件。接下来，情感分析技术利用用户的意见来识别交通事件的极性，从而有助于确定交通事件的准确状态。最后，使用 FastText 模型和带有 softmax 回归的 Bi-LSTM 对交通事件检测和状态分析进行训练。使用与交通相关的数据对所提出的框架进行评估，将 OLDA 和 Bi-LSTM 与现有的主题建模方法以及分别使用词嵌入模型的传统分类器进行比较。我们的系统的准确率达到 97%，优于最先进的方法。这一发现表明，与其他现有系统相比，所提出的系统在交通事件检测和状态分析方面更加高效。
