Current urbanization trends are leading to heightened demand of smarter technologies to facilitate a variety of applications in intelligent transportation systems. Automated crowdsensing constitutes a strong base for ITS applications by providing novel and rich data streams regarding congestion tracking and real-time navigation. Along with these well-leveraged data streams, drivers and passengers tend to report traffic information to social media platforms. Despite their abundance, the use of social media data in ITS has gained more and more attention as of now. In this article, we develop an automated Natural Language Processing (NLP)-based framework to empower and complement traffic reporting solutions by text mining social media, extracting desired information, and generating alerts and warning for drivers. We employ the fine-tuned Bidirectional Encoder Representations from Transformers classification model to filer and classify data. Then, we apply the Question-Answering model to extract necessary information characterizing the reported incident such as its location, occurrence time, and nature of the incidents. Afterwards, we convert the collected information into alerts to be integrated into personal navigation assistants. Finally, we compare the recently posted incident reports from both official authorities and social media in order to provide more complete incident pictures and suggest some open research directions.

中文翻译：

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利用NLP处理的社交媒体数据增强实时交通报告系统的功能

当前的城市化趋势导致对更智能技术的需求不断增加，以促进智能交通系统中的各种应用。通过提供有关拥塞跟踪和实时导航的新颖而丰富的数据流，自动化的人群感知为ITS应用奠定了坚实的基础。除了这些充分利用的数据流外，驾驶员和乘客还倾向于将交通信息报告给社交媒体平台。尽管数量众多，但截至目前，ITS中使用社交媒体数据已引起越来越多的关注。在本文中，我们开发了一个基于自然语言处理（NLP）的自动化框架，以通过文本挖掘社交媒体，提取所需信息以及为驾驶员生成警报和警告来增强和补充交通报告解决方案。我们从变压器分类模型中使用经过微调的双向编码器表示形式来对数据进行过滤和分类。然后，我们使用问题解答模型来提取描述报告事件的必要信息，例如事件的位置，发生时间和事件的性质。之后，我们将收集的信息转换为警报，以集成到个人导航助手中。最后，我们比较官方机构和社交媒体最近发布的事件报告，以提供更完整的事件图片并提出一些开放的研究方向。发生时间和事件的性质。之后，我们将收集的信息转换为警报，以集成到个人导航助手中。最后，我们比较官方机构和社交媒体最近发布的事件报告，以提供更完整的事件图片并提出一些开放的研究方向。发生时间和事件的性质。之后，我们将收集的信息转换为警报，以集成到个人导航助手中。最后，我们比较官方机构和社交媒体最近发布的事件报告，以提供更完整的事件图片并提出一些开放的研究方向。