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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

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

Jisung Kim¹, Amber Brooke Trueblood², Hye-Chung Kum³

¹Mobility Division, Transportation Planning, Texas A&M Transportation Institute, College Station, Texas.

Traffic Injury Prevention

|November 18, 2020

Summary

Machine learning models accurately identify agricultural crashes using crash narratives, outperforming traditional methods. This approach reduces manual data review and improves classification accuracy for farm equipment accidents.

Keywords:

Machine learning agricultural crashes bag-of-words crash narratives document classification algorithms

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Area of Science:

Traffic safety research
Data science applications in transportation
Agricultural engineering

Background:

Traditional crash data analysis relies on structured fields, which can lead to misclassification of vehicle types, including farm equipment.
Accurate identification of agricultural crashes is crucial for targeted safety interventions and policy development.

Purpose of the Study:

To investigate the efficacy of machine learning (ML) methods for identifying agricultural crashes using narrative text from crash reports.
To assess the transferability of ML models to different datasets, including future years and other states.

Main Methods:

Exploration of various data representations, such as bag-of-words (BoW) and bag-of-keywords (BoK).
Application of document classification algorithms including support vector machine (SVM) and multinomial Naïve Bayes classifier (MNB).
Training and testing models using crash narratives from Texas and Louisiana across different time periods.

Main Results:

Machine learning models, particularly bag-of-keywords with support vector classification (BoK-SVC) and multinomial Naïve Bayes (BoK-MNB), demonstrated superior predictive performance compared to baseline rule-based algorithms.
Models trained on Texas data achieved F1 scores up to 0.89, while models trained on Louisiana data reached an F1 score of 0.89 for BoK-MNB.
Models trained on combined Texas and Louisiana data showed strong predictive power for both states' future test data, with F1 scores reaching 0.90 for Texas and 0.94 for Louisiana.

Conclusions:

Machine learning methodologies offer a promising approach to enhance the accuracy of agricultural crash identification.
These ML methods have the potential to significantly reduce the manual effort required for keyword list development and narrative review in crash data analysis.
The study highlights the feasibility of applying ML models across different geographical regions and timeframes for improved traffic safety data analysis.