A semi-automated tool for identifying agricultural roadway crashes in crash narratives.
A semi-automated tool for identifying agricultural roadway crashes in crash narratives.
复制标题
一种半自动化工具，用于识别事故叙述中的农业道路事故。
DOI：
10.1080/15389588.2019.1599873
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发表时间：
2019
影响因子：
2
通讯作者：
Shipp,EvaMonique
中科院分区：
文献类型：
--
作者：
Trueblood,AmberBrooke;Pant,Ashesh;Kim,Jisung;Kum,Hye-Chung;Perez,Marcelina;Das,Subasish;Shipp,EvaMonique
目的：碰撞报告包含预编码的结构化数据字段和碰撞叙述，可以作为结构化数据中不包含的丰富信息的来源。这种叙述可以用于识别弱势道路使用者，如农业工人。然而，使用叙述通常需要人工审查，这既耗时又昂贵。本研究的目的是开发一个简单的和相对便宜的，半自动化的工具，用于筛选崩溃的叙述和加快过程中确定崩溃的具体特点，如农业crashs.Methods：崩溃记录路易斯安那州从2010年至2015年，从路易斯安那州交通部（LaDOTD）。提取具有叙述的记录，并按溶剂类型分层。大多数分析集中在农用设备的车辆类型（T型）上。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词列表，即纳入列表和排除列表。接下来，在Microsoft Excel中开发了一个半自动化工具，以确定农业崩溃。最后，使用通过人工审查确定的农业叙述的黄金标准集评估该工具的性能。结果：该工具减少了搜索空间（例如，需要人工审查的叙述数量），根据研究问题，需要人工审查的叙述从6.7%到59.4%。敏感性很高，96.1%的农业崩溃叙述被正确分类。黄金标准的农业叙述，58.3%包括设备关键字和72.8%包括一个农场设备brands.Conclusion：本文提供的信息如何崩溃的叙述可以补充结构化的崩溃数据。它还提供了一个易于实施的方法，以促进纳入叙述到安全研究沿着的关键字列表，以确定农业崩溃。
Objective: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.Methods: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.Results: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.Conclusion: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.