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Observational models based on reported crash history are the most common measures for identifying unsafe sites for priority intervention. Observational models are good for predicting higher-severity crashes but ignore higher-risk vehicle interactions (e.g., near misses) that failed to result in crashes that are reported in historical data. Proponents of microscopic simulation models argue that failure to recognize these higher-risk interactions can significantly understate the safety problem at a given site and lead to misallocation of scarce treatment funds. This paper takes the position that a complete understanding of the safety problem at a given site can emerge only if both crash potential and traffic conflicts are taken into account. A priority-ranking model that integrates estimates from observational crash prediction models into an analysis of traffic conflicts is presented. Traffic conflicts were based on simulated vehicle interactions and deceleration requirements for different traffic scenarios. The suitability of the approach for priority ranking of sites was assessed with six ranking approaches: crash frequency, empirical Bayes, potential for safety improvement, conflict frequency, conflict rate (sum and cross product of traffic volume), and integrated model. Priority ranking was evaluated with five test criteria: site consistency, method consistency, rank difference, total rank score, and sensitivity and specificity. These models were applied to a sample of 58 signalized intersections from Toronto, Ontario, Canada, for the period from 1999 to 2006. The integrated model was found to yield better results for the five evaluation criteria; this result suggests that the proposed approach has promise.

This paper examines the transferability of the Safety Performance Function (SPF) of the Highway Safety Manual (HSM) and other 10 international SPFs for total crashes on rural multi-lane divided roads in Egypt. Four segmentation approaches are assessed in the transferability of the international SPFs, namely: (1) one-kilometer segments (S1); (2) homogenous sections (S2); (3) variable segments with respect to the presence of curvatures (S3); and (4) variable segments with respect to the presence of both curvatures and U-turns (S4). The Mean Absolute Deviation (MAD), Mean Prediction Bias (MPB), Mean Absolute Percentage Error (MAPE), Pearson χ2 statistic, and Z-score parameters are used to evaluate the performance of the transferred models. The overdispersion parameter (k) for each transferred model and each segmentation approach is recalibrated using the local data by the maximum likelihood method. Before estimating the transferability calibration factor (Cr), three methods were used to adjust the local crash prediction of the transferred models, namely: (1) the HSM default crash modification factors (CMFs); (2) local CMFs; and (3) recalibrating the constant term of the transferred model. The latter method is found to outperform the first two methods. Besides, the results show that the segmentation method would affect the performance of the transferability process. Moreover, the Italian SPFs based on the S1 segmentation method outperforms the HSM and all of the investigated international SPFs for transferring their models to the Egyptian rural roads.