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On the interpretability of machine learning methods in crash frequency modeling and crash modification factor

Xiao Wen1, Yuanchang Xie1, Liming Jiang1

  • 1Department of Civil and Environmental Engineering, University of Massachusetts Lowell, United States.

Accident; Analysis and Prevention
|February 24, 2022
PubMed
Summary

Machine learning interpretability is crucial for understanding crash risk factors. SHapley Additive exPlanations (SHAP) effectively visualizes these relationships, aiding in developing safety countermeasures.

Keywords:
Crash frequencyCrash modification factorMachine learningModel interpretationSHAPSensitivity analysis

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Area of Science:

  • Traffic Safety Research
  • Machine Learning Applications

Background:

  • Machine learning (ML) models show promise in crash frequency studies.
  • Understanding the relationship between risk factors and crash frequency is vital for developing safety countermeasures.
  • A comprehensive summary of ML interpretation methods for crash frequency modeling is lacking.

Purpose of the Study:

  • To critically evaluate and compare model-based and post-hoc ML interpretation methods for crash frequency modeling.
  • To provide guidance for safety researchers and practitioners on selecting appropriate ML interpretation techniques.
  • To demonstrate the utility of ML and SHAP for deriving Crash Modification Factors (CMF).

Main Methods:

  • Evaluation of Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS), Local Interpretable Model-agnostic Explanations (LIME), Local Sensitivity Analysis (LSA), Partial Dependence Plots (PDP), Global Sensitivity Analysis (GSA), and SHapley Additive exPlanations (SHAP).
  • Comparison of methods based on their ability to reveal interaction relationships and handle correlated risk factors.
  • Demonstration of SHAP for visualizing detailed relationships between crash outcomes and risk factors.

Main Results:

  • Model-based methods fail to capture detailed interaction relationships.
  • LIME offers only prediction-level analysis; LSA and PDP assume independent risk factors.
  • GSA and SHAP can handle correlated risk factors, but only SHAP provides detailed visualizations.
  • SHAP facilitates the derivation of Crash Modification Factors (CMF).

Conclusions:

  • SHAP is a superior method for interpreting ML models in crash frequency analysis due to its ability to visualize detailed relationships and handle factor correlations.
  • Effective ML model interpretation is essential for developing reliable safety countermeasures.
  • Researchers must distinguish between correlation and causation when using statistical and ML models for safety analysis.