# A Novel Methodology for Planning Urban Road Safety Interventions
## 3 Novel Methodology
### Black Spots Identification and Risk Quantification
Factors like lane width, visibility, pedestrian crossings, and traffic density contribute to the risk score. In this study, iRAP's ViDA platform is adopted to calculate risk scores by using georeferenced images and field data. The star rating score (\(SRS\)) developed by iRAP quantifies the relative risk of fatal and serious injuries for each road user (drivers, motorcyclists, cyclists, and pedestrians) across different road segments. It is calculated by summing risk scores for various crash types using Equation (1). \[SRS=\sum Crash\ Type\ Scores \tag{1}\]  
These scores represent the risk of specific crash types based on road and traffic conditions. Common crash types include the following:  
* Motor Vehicles (Run-off-road, head-on collisions, intersection/access-point crashes);  
Figure 4: Risk matrix [26]. * Motorcycles (Run-off-road, head-on collisions, intersection crashes, and roadside incidents);
* Cyclists and Pedestrians (Roadside crashes, intersection crashes, and crossing-related incidents). Each _Crash Type Score_ is influenced by multiple _Risk Factors_, determined by road attributes and calculated using Equation (2). \[C\text{{rash Type Scores}}=\prod\text{{Risk Factors}} \tag{2}\]  
_Risk Factor_ categories are probability (evaluates the likelihood of an incident based on road attributes); severity (assesses the potential harm caused by an incident); operating speed (considers how speed variations affect risk); traffic flow (accounts for the influence of road usage by other users; and median crossing (examines the likelihood of vehicles crossing the median, particularly in head-on or run-off-road incidents, relevant for drivers and motorcyclists). The numerical result of SRS is translated into a five-star scale, with each star represented by a distinct color ranging from black (worst) to green (best) (see Table 1). This color-coding enhances the visual impact and clarity of the ratings, allowing for quick identification of the most critical safety concerns. When proactive results differ from reactive findings, further investigation is conducted to identify non-infrastructural causes, such as recurring human errors, through field visits. Regardless of proactive analysis outcomes, high-crash areas identified reactively are treated as high-priority for interventions. Reliable inputs are critical for accurate analysis, as incomplete or imprecise data compromises results. The iRAP models may struggle with complex road systems like those in Italy. Adjustments to local contexts are essential for effectiveness. Current methods lack detailed traffic flow analysis and do not account for elements like intersection geometry or traffic light timing, limiting risk assessment precision. The iRAP lacks specific criteria for evaluating cyclist crossings, potentially misrepresenting safety risks. Temporary reliance on pedestrian star ratings addresses this gap but introduces inconsistencies. While iRAP recognizes speed as a key factor in crash severity, its sensitivity to speed variations in low-traffic urban areas is limited. Tools for speed management, such as traffic-calming measures, enhance the model's utility.