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Measuring safety treatment effects using full Bayes non-linear safety performance intervention functions
Karim El-Basyouny1, Tarek Sayed
1Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada. karim.el-basyouny@ualberta.ca
Accident; Analysis and Prevention
|January 25, 2012
Summary
A new non-linear Koyck model improves safety performance function (SPF) analysis for before-after studies, offering better fits than linear models. This advanced method reveals significant reductions in collision counts following road improvements.
Area of Science:
- Traffic Safety Engineering
- Statistical Modeling
- Transportation Research
Background:
- Traditional Full Bayes linear intervention models in before-after safety studies assume linear slopes, limiting treatment profile representation.
- The need for more flexible models to capture complex treatment effects in safety studies is evident.
Purpose of the Study:
- To propose and evaluate a first-order autoregressive (AR1) safety performance function (SPF) using a non-linear Koyck model.
- To compare the Koyck model with the linear intervention model regarding inference, goodness-of-fit, and application in safety studies.
- To compute novelty, direct, and indirect treatment effects and provide expressions for their calculation.
Main Methods:
- Utilized a Poisson-lognormal (PLN) hierarchy with both linear intervention and non-linear Koyck models.
- Extended models by incorporating random parameters to account for site correlations within comparison-treatment pairs.
- Applied models to evaluate safety performance of improved intersections in the Greater Vancouver area.
Main Results:
- The Koyck model demonstrated a wider variety of treatment profiles compared to the linear model.
- Incorporating random parameters significantly improved model fit and reduced extra-Poisson variation.
- The PLN Koyck model provided a substantially better fit than the Poisson-lognormal linear intervention (PLNI) model.
- Direct treatment effects showed a significant 12.3% reduction in collision counts under the PLN Koyck model, versus a non-significant 6.5% reduction under PLNI.
Conclusions:
- The non-linear Koyck model offers superior flexibility and fit for safety performance functions compared to linear models.
- Random parameters enhance the accuracy of SPFs by accounting for site correlations.
- The proposed PLN Koyck model is a more effective tool for analyzing the impact of road safety interventions.
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