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An R-Based Landscape Validation of a Competing Risk Model

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Heterogeneous ensemble learning for enhanced crash forecasts - A frequentist and machine learning based stacking

Numan Ahmad¹, Behram Wali², Asad J Khattak³

¹Department of Civil & Environmental Engineering, The University of Tennessee, Knoxville, TN 37996, USA.

Journal of Safety Research

|March 3, 2023

Summary

This summary is machine-generated.

This study enhances roadway safety by improving crash prediction accuracy using a stacking ensemble method. Stacking outperforms traditional models and individual machine learning techniques for more reliable safety forecasts.

Keywords:

Base-learners Count data models Crash frequency Crash prediction Machine learning Meta-learner Stacking

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Area of Science:

Transportation Engineering
Traffic Safety
Machine Learning Applications

Background:

Accurate crash frequency prediction is crucial for forecasting roadway safety.
Machine learning (ML) methods generally offer higher prediction accuracy than traditional statistical models.
Heterogeneous ensemble methods (HEM), such as stacking, are emerging as robust techniques for enhanced prediction.

Purpose of the Study:

To apply and evaluate the stacking ensemble method for modeling crash frequency on urban and suburban arterial roadway segments.
To compare the prediction performance of stacking against parametric statistical models (Poisson, negative binomial) and individual ML techniques (Decision Tree, Random Forest, Gradient Boosting).
To demonstrate how stacking, by optimally combining base-learners, mitigates prediction bias and improves reliability.

Main Methods:

The study utilized crash, traffic, and roadway inventory data from 2013-2017 for five-lane undivided arterial segments.
Data were divided into training, validation, and testing sets.
Stacking was implemented by training five base-learners, generating predictions on validation data, and then training a meta-learner with these predictions.

Main Results:

Statistical models indicated that crash frequency increases with commercial driveway density and decreases with offset distance to fixed objects.
Individual ML methods showed comparable variable importance.
Out-of-sample prediction comparisons confirmed the superior performance of the stacking method over alternative models.

Conclusions:

Stacking significantly enhances prediction accuracy compared to individual base-learners, leading to more reliable safety forecasts.
The stacking approach can help identify more appropriate and targeted safety countermeasures.
This method offers a practical and systemic approach to improving roadway safety analysis and planning.