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Examining imbalanced classification algorithms in predicting real-time traffic crash risk.

Yichuan Peng1, Chongyi Li1, Ke Wang1

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This study introduces a comprehensive approach to real-time traffic crash risk prediction, addressing imbalanced data challenges. The developed models, RCSMLP and Rusboost, significantly improve prediction accuracy for safer urban expressways.

Keywords:
Continuous data environmentImbalanced data classificationRCSMLPReal-time crash risk prediction modelsRusboost model

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Area of Science:

  • Traffic Safety Engineering
  • Machine Learning for Transportation
  • Data Science for Urban Mobility

Background:

  • Active Traffic Management (ATM) systems are crucial for urban expressway safety.
  • Accurate real-time crash risk prediction is essential for ATM system effectiveness.
  • Traffic crash data presents an Imbalanced Data Classification problem, hindering accurate prediction.

Purpose of the Study:

  • To develop a more accurate real-time traffic crash risk prediction model.
  • To address the limitations of previous studies focusing on single-level data or algorithm improvements.
  • To investigate a comprehensive imbalanced classification algorithm integrating data, output, and algorithm levels.

Main Methods:

  • Examined data-level methods: Under-sampling and Synthetic Minority Oversampling Technique (SMOTE).
  • Investigated algorithm-level methods: cost-sensitive Multi-Layer Perceptron (MLP) and Adaboost.
  • Incorporated output-level techniques: Youden index and Probability Calibration Method.
  • Constructed Random Sampling Cost-sensitive MLP (RCSMLP) and Rusboost models.

Main Results:

  • The RCSMLP model achieved 78.10% sensitivity and 81.44% specificity.
  • The Rusboost model demonstrated superior performance with an AUC of 0.892, 0.842 sensitivity, and 0.816 specificity.
  • Both models outperformed existing prediction models in handling imbalanced traffic crash data.

Conclusions:

  • A comprehensive, multi-level approach significantly enhances real-time traffic crash risk prediction accuracy.
  • The proposed methods, particularly RCSMLP and Rusboost, offer robust solutions for imbalanced classification in traffic safety.
  • The developed methodology is adaptable and applicable to various prediction models for real-time traffic risk assessment.