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Developing Crash Severity Model Handling Class Imbalance and Implementing Ordered Nature: Focusing on Elderly

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This study improved crash severity prediction models for older drivers by addressing imbalanced data and ordered crash severities. Ordered random forest models showed superior performance, especially for predicting severe crashes.

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Area of Science:

  • Traffic Safety
  • Machine Learning
  • Gerontology

Background:

  • Increasing attention on older driver crash risk due to demographic shifts.
  • Need for accurate crash severity prediction models for senior-involved incidents.
  • Challenges posed by imbalanced data and ordered crash severities in classification.

Purpose of the Study:

  • To investigate the impact of incorporating ordinality and handling imbalanced classes on crash severity prediction performance.
  • To compare the effectiveness of various machine learning classifiers in predicting senior-involved crash severity.
  • To identify optimal modeling strategies for accurate crash severity classification.

Main Methods:

  • Utilized vehicle crash data from Ohio, U.S.
  • Implemented and compared eight machine learning classifiers: logistic regression, ordered logistic regression, random forest, and ordered random forest.
  • Evaluated models with and without strategies for handling imbalanced class distribution.

Main Results:

  • Balancing strategies significantly improved the prediction of severe crashes.
  • The impact of implementing ordinal nature varied across different models.
  • The ordered random forest classifier without balancing achieved the highest overall prediction accuracy.
  • The ordered random forest classifier with balancing demonstrated superior performance in predicting the severest crashes.

Conclusions:

  • Handling imbalanced data is crucial for enhancing severe crash prediction.
  • Ordered random forest models, particularly with balancing, offer promising results for predicting severe crashes involving older drivers.
  • The choice of incorporating ordinality depends on the specific machine learning model and prediction goals.