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A series of macrolevel prediction models that would estimate the number of accidents in planning zones in the city of Toronto, Ontario, Canada, as a function of zonal characteristics were developed. A generalized linear modeling approach was used in which negative binomial regression models were developed separately for total accidents and for severe (fatal and nonfatal injury) accidents as a function of socio-economic and demographic, traffic demand, and network data variables. The variables that had significant effects on accident occurrence were the number of households, the number of major road kilometers, the number of vehicle kilometers traveled, intersection density, posted speed, and volume-capacity ratio. The geographic weighted regression approach was used to test spatial variations in the estimated parameters from zone to zone. Mixed results were obtained from that analysis.

The objective of the research presented here was to capture the relationship between public transit service configurations and the overall safety performance of signalized intersections in Toronto, Ontario, Canada. Negative binomial regression models were developed for this purpose for three sets of dependent variables: transit-involved collisions at signalized intersections with both regular traffic and transit service operations; total collisions at the same signalized intersections; and total collisions at all signalized intersections, including those without transit service. The models showed that annual average daily traffic, public transit and pedestrian traffic volumes, turn movement treatments, and transit features (such as public transit stop location, mode technology, and availability of transit signal priority technology) all have significant associations with public transit–related collisions at signalized intersections. Intersections with public transit service also tend to experience more collisions than otherwise similar intersections. The research helps to address intersection safety from two perspectives: ( a) it enables public transit providers to consider safety implications in the service planning process, and ( b) it enables transportation departments to assess signalized intersection safety for various configurations of surface transit services by taking into consideration their interaction with the general traffic stream.

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Recently, several attempts have been made to develop collision prediction models in which spatial dependency is considered. These models recognize the local nature of spatial data by relaxing the regression analysis assumption that the error terms for each observation are independent. The primary objective of this study is to investigate an alternative technique for capturing the spatial variations in the relationship between the number of zonal collisions and potential transportation planning predictors. Spatial relationships are incorporated into the full Bayesian semiparametric additive modeling framework through the covariance of the error terms. The secondary objective of this research study is to build on knowledge of comparing the accuracy of full Bayesian models to that of generalized linear and geographically weighted Poisson regression models. The spatial covariates from the full Bayesian semiparametric additive model indicate that collision frequencies in traffic analysis zones are spatially correlated. The results of accuracy comparison indicate that the spatial models perform better than the conventional generalized linear models. However, mixed results are obtained when the FBSA models were compared to the geographically weighted Poisson regression models.