# Determining the Safety Level of State Roads: An Italian Case Study
## 7 Conclusions  
This study aimed to assess the effectiveness of various safety performance indicators in evaluating the actual safety level of the road network. This evaluation is crucial for prioritizing road sections for field inspections and, if necessary, subsequent maintenance. Traditional safety indicators based on observed crashes are subject to regression to the mean and influenced by the stochastic nature of accidents. Therefore, safety indicators based on the US Highway Safety Manual approach were also considered. The application of the US HSM model to the Italian context required calibration to suit different average conditions. The calibration result can be applied to similar contexts unless significant boundary condition changes occur. The study evaluated six different safety performance indicators across elementary road sections of constant length (1 km). These indicators enabled different safety rankings for the elementary sections. In particular, two safety indicators are compared: the excess of expected crashes over predicted crashes, based on the US HSM model, and the Safety Potential (SAPO) proposed by the Italian guidelines and relying solely on the observed crashes. As a result, the excess of expected crashes indicator is more conservative in identifying critical sections, with some sections classified as dangerous by this indicator being ignored by the SAPO. This disparity might be due to the fact that the SAPO is affected by the regression to the mean of observed crashes and is mainly focused on road fatalities. Moreover, a local SAPO is proposed as an alternative to the safety indicator defined by the Italian guidelines (global SAPO). The local SAPO considers base cost density specific to roadway sections rather than relying on the national average value, as suggested by the Italian guidelines [5] for the global SAPO. In fact, the local SAPO shows a better capability to identify critical roadway sections with respect to the global one. The excess of crashes effectively identifies potentially critical conditions in sections with both fatalities and injuries, whereas SAPO prioritizes crashes with fatalities. The safety ranking is crucial for organizing field inspections, which are time-consuming but essential for determining the actual causes of accidents. Thus, safety indicators should be employed to assign a priority level to road sections. When available, it is always appropriate to rely on more than one safety indicator and possibly introduce the safety indicators based on the HSM procedure, which allow to overcome the issue related to the regression to the mean. A possible approach to assigning priority based on the excess of crashes and SAPO is detailed in Section 6.4. However, the proposed solution is based on just two safety performance indicators and represents one of the possible options. In fact, a more extensive and detailed analysis would be needed to include additional indicators in the method or even to adjust the approach according to the available indicators in order to refine the result and more precisely select the sites for on-site inspection. Then field inspections can be conducted according to the assigned priority. In this way, if the on-site inspection reveals the presence of critical infrastructure conditions, maintenance interventions could be planned accordingly. Thanks to the safety ranking, it would be quicker and more immediate to focus attention on the most dangerous locations. Utilizing predictive methods allows for assessing the potential impacts of maintenance interventions before implementation. Cost-benefit analyses aid in selecting the most beneficial intervention among different alternatives and prioritizing urgent needs across different locations based on safety considerations. A further agenda could focus on refining these predictive approaches to optimize resource allocation and enhance safety outcomes across the road network. Conceptualization, M.C. and M.P. ; methodology, M.P. and E.T. ; software, A.A. and M.K. ; validation, E.T. and M.K.