Recently, macro-level collision prediction models that are consistent with aggregate travel demand models have been developed to provide empirical tools for planners and engineers to conduct proactive road safety planning. A common technique used for the calibration of safety planning models is the Generalized Linear Modeling procedure with the assumption of negative binomial or Poisson error distribution. In this technique, fixed coefficients that represent the average relationship between the dependent variable, typically number of collisions per traffic analysis zone, and each explanatory variable are estimated. However, the stationary relationship assumed may hide some important spatial factors of the number of collisions ata particular traffic analysis zone. Consequently, the accuracy of such models for explaining the relationship between the dependent variable and the explanatory variables may be suspect since collision frequency is likelyinfluenced by many spatially defined factors such as land use, demographic characteristics, and traffic volume patterns. The primary objective of this study is to investigate the spatial variations in the relationship between the number of zonal collisions and potential transportation planning predictors using the Geographically Weighted Poisson Regression modeling technique. The secondary objective is to compare the accuracy of Geographically Weighted Poisson Regression models to that of Generalized Linear Models. The results show that the Geographically Weighted Poisson Regression models perform better than the conventional Generalized Linear Models and therefore should be considered for safety planning analysis of transportation networks.
Samenvatting