A Semi-Automated Tool for Identifying Agricultural Roadway Crashes in Crash Narratives

Details

• Personal Author:
  Das S ; Kim J ; Kum H-C ; Pant A ; Perez M ; Shipp EM ; Trueblood, Amber B.
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Accidents, Occupational Agriculture Automation Automobiles Farmers Occupational Health Safety
• Keywords:
  Accident Analysis Accidents Agricultural Industry Agricultural Machinery Author Keywords: Agriculture Crash Narratives Farm Workers Injury Motor Vehicle Crashes Motor Vehicles Occupational Health Text Mining

  More +
• ISSN:
  1538-9588
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  OSHA Region 6 ; Texas
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  413-418
• Volume:
  20
• Issue:
  4
• NIOSHTIC Number:
  nn:20059920
• Citation:
  Traffic Inj Prev 2019 May; 20(4):413-418
• Contact Point Address:
  Amber Brooke Trueblood, Center for Transportation Safety, Texas A&M Transportation Institute, 2929 Research Parkway, Office 317, College Station, TX 77843
• Email:
  A-Trueblood@tti.tamu.edu
• Federal Fiscal Year:
  2019
• NORA Priority Area:
  Agriculture, Forestry and Fishing
• Performing Organization:
  University of Texas Health Center at Tyler
• Peer Reviewed:
  True
• Start Date:
  20010930
• Source Full Name:
  Traffic Injury Prevention
• End Date:
  20270929
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:c4355ab042d3313491586714b6359363f5199366b7fc0cb8ab2e0d955ecac1e296657aada1dddc7ccdcf2b45e4998ed3683f5ed8bbe1f61cbc9011fa9514b500
• Download URL:
  https://stacks.cdc.gov/view/cdc/223974/cdc_223974_DS1.pdf
• File Type:
  [PDF - 1.27 MB ]