Crash narrative classification: Identifying agricultural crashes using machine learning with curated keywords.

Journal: Traffic injury prevention
PMID: 33206551

Abstract

OBJECTIVE: Traditionally, structured or coded data fields from a crash report are the basis for identifying crashes involving different types of vehicles, such as farm equipment. However, using only the structured data can lead to misclassification of vehicle or crash type. The objective of the current article is to examine the use of machine learning methods for identifying agricultural crashes based on the crash narrative and to transfer the application of models to different settings (e.g., future years of data, other states).

Authors

  • Jisung Kim
    Mobility Division, Transportation Planning, Texas A&M Transportation Institute, College Station, Texas.
  • Amber Brooke Trueblood
    Center for Transportation Safety, Crash Analytics Team, Texas A&M Transportation Institute, College Station, Texas.
  • Hye-Chung Kum
    Population Informatics Lab, Department of Health Policy and Management, School of Public Health, Texas A&M University, College Station, Texas.
  • Eva M Shipp
    Center for Transportation Safety, Crash Analytics Team, Texas A&M Transportation Institute, College Station, Texas.

Keywords

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