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  3. Enhancing Work Zone Crash Severity Analysis: The Role Of Synthetic Minority Oversampling Technique In Balancing Minority Categories.
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  3. Enhancing Work Zone Crash Severity Analysis: The Role Of Synthetic Minority Oversampling Technique In Balancing Minority Categories.

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Enhancing work zone crash severity analysis: The role of synthetic minority oversampling technique in balancing

Muhammad Adeel1, Asad J Khattak1, Sabyasachee Mishra2

  • 1Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN, United States.

Accident; Analysis and Prevention
|September 28, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Aggressive driving, speeding, and drunk driving increase road work zone crash severity. Addressing imbalanced injury data reveals shifts in contributing factors, aiding safety improvements.

Keywords:
Aggressive drivingDrunk drivingInjury severityLighting conditionsPartial Proportional Odds (PPO) modelPosted speed limitRandom Forest (RF) modelSynthetic Minority Oversampling Technique (SMOTE)Weather conditionsWork zones (WZ) crashes

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Area of Science:

  • Transportation Engineering
  • Traffic Safety
  • Data Science

Background:

  • Road work zones (WZ) pose significant safety risks due to altered traffic conditions and reduced visibility.
  • Understanding human behavioral factors in WZ crash injury severity is crucial, especially with imbalanced crash data.
  • Imbalanced datasets, common in injury severity analysis, often underrepresent severe outcomes, necessitating advanced techniques.

Purpose of the Study:

  • To investigate behavioral factors contributing to injury severity in road work zone (WZ) crashes.
  • To analyze how these factors change after addressing data imbalance using the Synthetic Minority Oversampling Technique (SMOTE).
  • To provide a framework for analyzing imbalanced crash injury data.

Main Methods:

  • Utilized a dataset of 7,855 WZ crashes in Tennessee (2018-2022).
  • Applied frequentist methods and a machine learning approach incorporating SMOTE to handle data imbalance.
  • Examined changes in the importance of contributing factors after balancing minority injury severity categories.

    • Main Results:

    • Aggressive driving, overspeeding, and drunk driving were identified as significant factors increasing injury severity.
    • Balancing the imbalanced dataset altered the relative importance of various contributing factors to crash injury severity.
    • The study demonstrated the effectiveness of SMOTE in improving inferences from imbalanced crash data.

    Conclusions:

    • Behavioral factors like aggressive driving and impaired driving substantially impact WZ crash injury severity.
    • Adjusting for data imbalance is critical for a comprehensive understanding of crash injury severity.
    • The findings offer valuable insights for traffic safety engineers and policymakers to enhance WZ safety measures.