A semi-automated tool for identifying agricultural roadway crashes in crash narratives. (19th May 2019)

• Record Type: Journal Article
• Authors: Trueblood, Amber Brooke; Pant, Ashesh; Kim, Jisung; Kum, Hye-Chung; Perez, Marcelina; Das, Subasish; Shipp, Eva Monique
• Is Part Of: Traffic injury prevention. Volume 20:Number 4(2019)
• Page Start: 413
• Page End: 418
• Publication Date: 2019-05-19
• Subjects: Agriculture -- injury -- motor vehicle crashes -- occupational health -- text mining -- crash narratives; Traffic safety -- Periodicals; Traffic accidents -- Periodicals; Wounds and injuries -- Prevention -- Periodicals; 363.125
• DOI: 10.1080/15389588.2019.1599873
• Languages: English
• ISSNs: 1538-9588

Abstract

Abstract: 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.