The Highway Safety Manual (HSM) provides an algorithm, and associated knowledge, for predicting crashes for different facility types. This algorithm requires calibration to current local conditions to enhance transferability, using a procedure that is prescribed in the HSM. However, there is no procedure for assessing transferability. To fill this void, this paper is focused on the methodology for assessing the transferability of the key HSM algorithm components, the baseline Safety Performance Function (SPF) and the Crash Modification Factors (CMFs), using the Italian road network for an illustrative case study. The calibration of the HSM crash prediction model is investigated with a dataset for two-lane two-way rural highways, to demonstrate tools that could be used by jurisdictions around the world for assessing the validity and compatibility of the CMFs and base models, as well as the performance of the complete algorithm. A comparison with the results from a similar study carried out in Canada is provided in supplementing the conclusions on the transferability of the HSM algorithm outside the United States.* factor not applied when AADT is in [millions of vehicle per year; ** conversion factor from km to mi *** HSM default proportion of F+I crashes for rural 2U segments

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The present study introduces a new technique for multivariate identification and ranking of hot spots based on the Mahalanobis distance. This approach aims to extend the univariate potential for safety improvement to cases in which multiple response variables are modeled jointly. Because the literature shows that ranking techniques based on Bayesian methods are superior to those that rely simply on the observed collision count, the proposed method was developed in a full Bayesian (FB) context. The new technique involves the following steps: (a) applying multivariate Poisson–lognormal regression models to the data by means of the FB method, (b) using the estimates of the Poisson posterior means for each site to compute the multivariate (Mahalanobis) distance from what is the normal Poisson mean for similar sites, and (c) preparing an ordered list of potentially hazardous sites. This method was applied to a sample of 173 signalized intersections in the city of Vancouver, British Columbia, Canada, for the years 2008 to 2012. The study also examines the consistency of the technique itself by analyzing the mathematical intersection of ranked sites identified in subsequent time periods. Finally, the consistency of the multivariate FB ranking was assessed against the independent (separate) univariate one that is still dominant in road safety evaluations.