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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Application of a model-based recursive partitioning algorithm to predict crash frequency.

Houjun Tang¹, Eric T Donnell¹

¹Department of Civil and Environmental Engineering, The Pennsylvania State University, 212 Sackett Building, University Park, PA 16802, United States.

Accident; Analysis and Prevention

|August 26, 2019

Summary

Model-based recursive partitioning (MOB) improves crash frequency analysis by offering better data fitness and prediction accuracy than traditional negative binomial (NB) models. This method aids in identifying key traffic safety covariates.

Keywords:

Crash frequency Data mining Heterogeneity Model-based recursive partitioning Roadway safety

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Area of Science:

Traffic Safety
Statistical Modeling
Data Mining

Background:

Count regression models are standard for estimating crash frequencies.
Data mining algorithms offer non-parametric alternatives but lack statistical inference.
Existing methods struggle with evaluating variable associations in crash frequency analysis.

Purpose of the Study:

To explore the potential of the model-based recursive partitioning (MOB) algorithm as an alternative to parametric methods in crash frequency analysis.
To compare the MOB algorithm's performance against standard and advanced negative binomial (NB) models.
To assess the MOB algorithm's ability to identify covariates and evaluate variable associations.

Main Methods:

Application of the model-based recursive partitioning (MOB) algorithm for crash frequency analysis.
Comparison of a standard negative binomial (NB) model, MOB-NB model, adjusted NB models, and a random parameters NB (RPNB) model.
Evaluation based on data fitness, statistical association, and predictive power using 8 years of Pennsylvania highway data.

Main Results:

The MOB-NB model demonstrated superior data fitness compared to other NB models.
MOB-NB performance was comparable to the RPNB model, suggesting it captures unobserved heterogeneity.
Key covariates like passing zones and speed limits were identified by MOB, influencing variable effects across subgroups.

Conclusions:

The MOB algorithm is a promising alternative for identifying covariates, assessing variable associations, and improving predictions in crash frequency studies.
MOB-NB models offer enhanced data fitness and predictive accuracy.
MOB facilitates a deeper understanding of how variables influence crash frequencies in different data subgroups.