Despite rapid advances in automated text processing, many related tasks in transit and other transportation agencies are still performed manually. For example, incident management reports are often manually processed and subsequently stored in a standardized format for later use. The information contained in such reports can be valuable for many reasons: identification of issues with response actions, underlying causes of each incident, impacts on the system, etc. In this article, we develop a comprehensive, pragmatic automated framework for analyzing rail incident reports to support a wide range of applications and functions, depending on the constraints of the available data. The objectives are twofold: a) extract information that is required in the standard report forms (automation), and b) extract other useful content and insights from the unstructured text in the original report that would have otherwise been lost/ignored (knowledge discovery). The approach is demonstrated through a case study involving analysis of 23,728 records of general incidents in the London Underground (LU). The results show that it is possible to automatically extract delays, impacts on trains, mitigating strategies, underlying incident causes, and insights related to the potential actions and causes, as well as accurate classification of incidents into predefined categories.

中文翻译：

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文本数据的机器学习增强分析：在交通中断管理中的应用

尽管在自动文本处理方面取得了飞速的进步，但在运输和其他运输代理中的许多相关任务仍然是手动执行的。例如，事件管理报告通常是手动处理的，随后以标准化格式存储，以备后用。此类报告中包含的信息由于许多原因可能很有价值：识别响应措施的问题，每个事件的根本原因，对系统的影响等。在本文中，我们开发了一个全面，实用的自动化框架来分析铁路事件报告支持各种应用程序和功能，具体取决于可用数据的限制。目标是双重的：a）提取标准报告表（自动化）中所需的信息，b）从原始报告中的非结构化文本中提取其他有用的内容和见解，否则它们将被丢失/忽略（知识发现）。通过案例研究证明了该方法，该案例研究分析了伦敦地铁（LU）的23,728条一般事件记录。结果表明，可以自动提取延误，对列车的影响，缓解策略，潜在的事故原因以及与潜在动作和原因相关的见解，以及将事故准确分类为预定义的类别。