A semi-automated tool for identifying agricultural roadway crashes in crash narratives-Reference-Cited by-同舟云学术

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A semi-automated tool for identifying agricultural roadway crashes in crash narratives

Published:2019-05-10 Issue:4 Volume:20 Page:413-418
ISSN:1538-9588
Container-title:Traffic Injury Prevention
language:en
Short-container-title:Traffic Injury Prevention

Author:

Trueblood Amber Brooke¹^ORCID,Pant Ashesh¹,Kim Jisung²,Kum Hye-Chung³^ORCID,Perez Marcelina¹,Das Subasish⁴^ORCID,Shipp Eva Monique¹^ORCID

Affiliation:

1. Center for Transportation Safety, Texas A&M Transportation Institute, College Station, Texas;

2. Transportation Planning, Texas A&M Transportation Institute, College Station, Texas;

3. Department of Health Policy & Management, Texas A&M University, College Station, Texas;

4. Traffic Operations & Roadway Safety Division, Texas A&M Transportation Institute, College Station, Texas

Funder

National Institute for Occupational Safety and Health

Publisher

Informa UK Limited

Subject

Public Health, Environmental and Occupational Health,Safety Research

Link

https://www.tandfonline.com/doi/pdf/10.1080/15389588.2019.1599873

Reference20 articles.

1. Analysis of Tractor-related Deaths in North Carolina From 1979 to 1988

2. Narrative text analysis of Kentucky tractor fatality reports

3. Risk factors for a farm vehicle public road crash

4. Supervised association rules mining on pedestrian crashes in urban areas: identifying patterns for appropriate countermeasures

5. An investigation of the speeding-related crash designation through crash narrative reviews sampled via logistic regression

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Estimating occupation-related crashes in light and medium size vehicles in Kentucky: A text mining and data linkage approach;Accident Analysis & Prevention;2024-11

2. Using motor vehicle crash records for injury surveillance and research in agriculture and forestry;Journal of Safety Research;2023-09

3. Application of text mining techniques to identify actual wrong-way driving (WWD) crashes in police reports;International Journal of Transportation Science and Technology;2022-12

4. Identification and analysis of misclassified work-zone crashes using text mining techniques;Accident Analysis & Prevention;2021-09

5. Crash narrative classification: Identifying agricultural crashes using machine learning with curated keywords;Traffic Injury Prevention;2020-11-18

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