Historically, the traditional planning process has not given much attention to the road safety evaluation of development plans. To make an informed, defensible, and proactive choice between alternative plans and their safety implications, it is necessary to have a procedure for estimating and evaluating safety performance. A procedure is required for examining the influence of the urban network development on road safety, and in particular, determining the effects of the many variables that affect safety in urban planning. Safety planning models can provide a decision-support tool that facilitates the assessment of the safety implications of alternative network plans. The first objective of this research study is to develop safety planning models that are consistent with the regional models commonly used for urban transportation planning. Geographically weighted Poisson regression (GWPR), full-Bayesian semiparametric additive (FBSA), and traditional generalized linear modelling (GLM) techniques are used to develop the models. The study evaluates how well each model is able to handle spatial variations in the relationship between collision explanatory variables and the number of collisions in a zone. The evaluation uses measures of goodness of fit (GOF) and finds that the GWPR and FBSA models perform much better than the conventional GLM approach. There is little difference between the GOF values for the FBSA and GWPR models. The second objective of this research study is to examine the temporal transferability of the safety planning models and alternative updating methods. The updating procedures examine the Bayesian approach and application of calibration factors. The results show that the models are not temporally transferable in a strict statistical sense. However, relative measures of transferability indicate that the transferred models yield useful information in the application context. The results also show that the updated safety planning models using the Bayesian approach predict the number of collisions better than the calibration factor procedure.

Historically, the traditional planning process has not given much attention to the road safety evaluation of development plans. To make an informed, defensible, and proactive choice between alternative plans and their safety implications, it is necessary to have a procedure for estimating and evaluating safety performance. A procedure is required for examining the influence of the urban network development on road safety, and in particular, determining the effects of the many variables that affect safety in urban planning. Safety planning models can provide a decision-support tool that facilitates the assessment of the safety implications of alternative network plans. The first objective of this research study is to develop safety planning models that are consistent with the regional models commonly used for urban transportation planning. Geographically weighted Poisson regression (GWPR), full-Bayesian semiparametric additive (FBSA), and traditional generalized linear modelling (GLM) techniques are used to develop the models. The study evaluates how well each model is able to handle spatial variations in the relationship between collision explanatory variables and the number of collisions in a zone. The evaluation uses measures of goodness of fit (GOF) and finds that the GWPR and FBSA models perform much better than the conventional GLM approach. There is little difference between the GOF values for the FBSA and GWPR models. The second objective of this research study is to examine the temporal transferability of the safety planning models and alternative updating methods. The updating procedures examine the Bayesian approach and application of calibration factors. The results show that the models are not temporally transferable in a strict statistical sense. However, relative measures of transferability indicate that the transferred models yield useful information in the application context. The results also show that the updated safety planning models using the Bayesian approach predict the number of collisions better than the calibration factor procedure.