Before–After Safety Evaluation Using Full Bayesian Macroscopic Multivariate and Spatial Models

Abstract

Many studies have addressed spatial correlation in traffic collision modeling. It has been generally concluded that the inclusion of spatial correlation improves model goodness of fit and the precision of parameter estimates. However, the application in before–after safety evaluation has rarely been documented in the traffic safety literature. The objectives of the presented study were to ( a) apply both the univariate and multivariate full Bayesian (FB) spatial models in before–after safety evaluation and ( b) compare the results with those of nonspatial FB models. A reduction of the posted speed limit in urban residential neighborhoods in Edmonton, Alberta, Canada was used as a case study for the before–after safety evaluation. Yearly collision data and other neighborhood characteristics data were collected for a group of treated and reference neighborhoods to develop macroscopic models. The four models considered in this study were ( a) Poisson–lognormal, ( b) Poisson–lognormal with conditional autoregressive (CAR) distribution, ( c) multivariate Poisson–lognormal, and ( d) multivariate Poisson–lognormal with CAR distribution. The results showed that the multivariate Poisson–lognormal with CAR distribution model for collision severities outperformed the other three models according to the deviance information criteria. Parameter estimates showed slight differences across the models. However, for the current data set, the results of the before–after safety evaluation showed similar findings across the models. Estimated collision reductions were 13%, 24%, and 12% for total, severe, and property-damage-only collisions, respectively.