A full Bayes multivariate intervention model with random parameters among matched pairs for before–after safety evaluation

The objective of this study is to evaluate the safety performance of a sample of intersections that have been improved with the implementation of certain safety countermeasures in the Greater Vancouver area. A full Bayes approach is utilized to determine the effectiveness of the improvements using a before–after design with matched (yoked) comparison groups. A multivariate Poisson‐lognormal intervention model is used for the analysis of crash counts by severity levels. The model is extended to incorporate random parameters to account for the correlation between sites within comparison‐treatment pairs. The full Bayes analysis revealed that incorporating such design features as matched comparison groups in the specification of safety performance functions can significantly improve the fit, while reducing the estimates of the extra‐Poisson variation. As well, such extended models can be used to account for heterogeneity due to unobserved road geometrics, traffic characteristics, environmental factors and driver behavior. The results showed that the overall odds ratios for injuries and fatalities (I + F) and property damage only (PDO) imply significant reductions in predicted crash counts of 23% and 15%, respectively. The corresponding credible intervals were (12%, 33%) and (6%, 24%) at the 0.95 confidence level. The majority of the site‐level odds ratio exhibited reductions in both I + F and PDO predicted crash counts. However, only some of these reductions were significant. As well, the effectiveness of the treatment seems to vary by severity level from one location to another. For I + F, the crash reduction factors were 29%, 15% and 21% for improving signal visibility, left turn phase improvement and left turn lane installation, respectively. The corresponding crash reduction factors for PDO were 21%, 4% and 20%, respectively.