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Crash Injury Severity Prediction Using an Ordinal Classification Machine Learning Approach.

Shengxue Zhu1, Ke Wang2, Chongyi Li2

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This study introduces an improved ordinal classification method for predicting traffic crash injury severity. By addressing rank inconsistencies, the new approach enhances prediction accuracy compared to existing methods.

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Area of Science:

  • Traffic safety research
  • Machine learning applications in transportation

Background:

  • Nominal classification methods often overlook injury severity level order, leading to suboptimal traffic crash predictions.
  • Existing ordinal classification techniques face challenges with rank inconsistency and non-monotonicity.

Purpose of the Study:

  • To propose and evaluate a novel ordinal classification approach for predicting traffic crash injury severity.
  • To compare the proposed method against existing machine learning classifiers, including neural networks, XGBoost, and SVM.
  • To enhance ordinal classification performance by addressing class imbalance and improving probability estimation.

Main Methods:

  • Implemented an ordinal classification framework incorporating probability calibration and optimal probability threshold moving.
  • Utilized a severity category-combination method with oversampling to mitigate class imbalance in crash data.
  • Compared performance against nominal classifiers (neural network, XGBoost, SVM) and other ordinal methods.
  • Identified key factors influencing injury severity using permutation feature importance.

Main Results:

  • The proposed ordinal classification approach demonstrated superior performance, satisfying rank consistency and monotonicity requirements.
  • Statistical significance tests confirmed the superiority of the new method over existing nominal and ordinal classification techniques.
  • Converting injury severity into three classes (minor, moderate, serious) significantly improved prediction precision.

Conclusions:

  • The developed ordinal classification method offers a more accurate and reliable approach to predicting traffic crash injury severity.
  • Addressing class imbalance and refining probability estimation are crucial for improving ordinal classification in this domain.
  • Simplifying severity levels into broader categories can enhance predictive model performance.