# Determining the Safety Level of State Roads: An Italian Case Study
## 2 Literature Review  
Improving road safety is a priority both at the Italian national level [3; 5] and the international level [16]. To enhance road infrastructure safety, it is necessary to develop a method that identifies critical conditions for road users, allowing for intervention even before serious consequences arise. This is the general principle of the safe system approach, which aims to create intrinsically safe roads that mitigate the consequences of human error. To achieve this, various international frameworks have been proposed to address road safety. Several countries have developed specific guidelines for road safety and maintenance management, including the USA [6], Australia [17; 18; 19; 20], Canada [21; 22], the UK [23; 24], and Switzerland [25]. In addition, numerous international associations are developing useful safety management tools, such as iRAP (International Road Assessment Programme) [26] and PIARC (World Road Association) [27]. The most widely used framework is the American approach detailed in the HSM, first published in 2010 [6] by the American Association of State Highway and Transportation Officials (AASHTO). An update of the HSM is also planned, and a preliminary document has already been shared [28]. This update will facilitate the application and adaptation of the US HSM methodology to various international contexts. The main improvement related to this update is that the predictive models are differentiated for levels of accident severity. This makes it easier to adapt the US method to the Italian reality, where only accidents with fatalities and injuries are recorded, as opposed to the US, where accidents with property damage are also considered. The safety level of a road or road network can be quantified by the number of accidents that occur on it. However, accidents are rare, random, and independent events subject to regression to the mean, so relying solely on observed accidents can distort reality. This issue can be addressed by using an empirical Bayesian (EB) approach that quantifies the expected number of accidents, combining observed accidents with the ones obtained by predictive models based on the real site conditions (i.e., traffic volume and geometric-functional road characteristics). The predictive model referenced below is the one used in the HSM. In order to transfer the HSM methodology to other contexts, as mentioned previously, two approaches can be followed. The first approach consists of developing new safety performance functions for the specific case study, as already happened for a case study conducted in Italy [29]. The second approach, which is selected for the present study, consists of adapting HSM safety performance functions to the study context through calibration. In this case, the reliability of the model depends on the accuracy of the calibration process that is required to adapt the HSM to the Italian context. The transferability of the HSM model has been repeatedly assessed by international studies [30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41]. For the optimization of the calibration procedure and the evaluation of its goodness, a specific tool has been developed [42]. In Italy, the HSM model has already been adopted for various purposes [43; 44; 45; 46; 47], showing that the model is not always immediately transferable to the Italian context. Thus, a stratified calibration process for different geographical areas might be appropriate. In this way, the model would be applied to geographical sub-areas with homogeneous characteristics. This would make the model more functional, as the input data would be less scattered in terms of terrain, orographic characteristics, and behavioral habits. The appropriately calibrated model, applied across the entire managed road network, would enable the number of accidents on different sections of road to be estimated and particularly high-risk locations to be identified.