a Center for Transportation Safety, Texas A&M Transportation Institute , College Station , Texas.
b Transportation Planning , Texas A&M Transportation Institute , College Station , Texas.
Traffic Inj Prev. 2019;20(4):413-418. doi: 10.1080/15389588.2019.1599873. Epub 2019 May 10.
Crash reports contain precoded structured data fields and a crash narrative that can be a source of rich information not included in the structured data. The narrative can be useful for identifying vulnerable roadway users, such as agricultural workers. However, using the narratives often requires manual reviews that are time consuming and costly. The objective of this research was to develop a simple and relatively inexpensive, semi-automated tool for screening crash narratives and expediting the process of identifying crashes with specific characteristics, such as agricultural crashes. Crash records for Louisiana from 2010 to 2015 were obtained from the Louisiana Department of Transportation (LaDOTD). Records with narratives were extracted and stratified by vehicle type. The majority of analyses focused on a vehicle type of farm equipment (Type T). Two keyword lists, an inclusion list and an exclusion list, were created based on the published literature, subject-matter experts, and findings from a pilot project. Next, a semi-automated tool was developed in Microsoft Excel to identify agricultural crashes. Lastly, the tool's performance was assessed using a gold standard set of agricultural narratives identified through manual review. The tool reduced the search space (e.g., number of narratives that need manual review) for narratives requiring manual review from 6.7 to 59.4% depending on the research question. Sensitivity was high, with 96.1% of agricultural crash narratives being correctly classified. Of the gold standard agricultural narratives, 58.3% included an equipment keyword and 72.8% included a farm equipment brand. This article provides information on how crash narratives can supplement structured crash data. It also provides an easy-to-implement method to facilitate incorporating narratives into safety research along with keyword lists for identifying agricultural crashes.
碰撞报告包含预编码的结构化数据字段和碰撞描述，这些描述可以提供丰富的信息，而这些信息在结构化数据中可能并不包含。这些描述可用于识别弱势道路使用者，例如农业工人。然而，使用这些描述通常需要耗费大量时间和成本的人工审查。本研究的目的是开发一种简单且相对廉价的半自动化工具，用于筛选碰撞描述，并加速识别具有特定特征（如农业碰撞）的碰撞的过程。 从路易斯安那州交通部（LaDOTD）获取了 2010 年至 2015 年路易斯安那州的碰撞记录。提取并按车辆类型分层记录有描述的记录。大多数分析主要集中在一种车辆类型，即农用设备（Type T）。根据已发表的文献、主题专家和试点项目的结果，创建了两个关键字列表，一个包含列表和一个排除列表。接下来，在 Microsoft Excel 中开发了一个半自动化工具，用于识别农业碰撞。最后，使用通过手动审查确定的农业描述的黄金标准集来评估工具的性能。 该工具减少了需要手动审查的描述的搜索空间（例如，需要手动审查的描述数量），具体取决于研究问题，从 6.7%到 59.4%不等。敏感性很高，96.1%的农业碰撞描述被正确分类。在黄金标准的农业描述中，58.3%包含设备关键字，72.8%包含农业设备品牌。 本文提供了有关碰撞描述如何补充结构化碰撞数据的信息。它还提供了一种易于实施的方法，以促进将描述纳入安全研究，并提供了用于识别农业碰撞的关键字列表。
