Statistical models for estimating the safety status of transportation facilities have received great attention in the last two decades. These models also perform an important role in transportation safety planning as well as diagnoses of locations with high accident risks. However, the current methods largely rely on regression analyses and therefore they could ignore the multicollinearity characteristics of factors, which may provide additional information for enhancing the performance of forecasting models. This study seeks to develop more precise models for forecasting safety status as well as addressing the issue of multicollinearity of dataset. The proposed mathematical approach is indeed a discriminant analysis with respect to the goal of minimizing Bayes risks given multivariate distributions of factors. Based on this model, numerical analyses also perform with the application of a simulated dataset and an empirically observed dataset of traffic accidents in road segments. These examples essentially illustrate the process of Bayes risk minimization on predicating the safety status of road segments toward the objective of smallest misclassification rate. The paper finally concludes with a discussion of this methodology and several important avenues for future studies are also provided.
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