# A Novel Methodology for Planning Urban Road Safety Interventions
## 3 Novel Methodology
### Black Spots Identification and Risk Quantification  
The identification of high-risk areas in a road network can leverage both reactive and proactive approaches. The reactive approach focuses on analyzing historical crash data to objectively assess risk. However, its effectiveness is limited in contexts with insufficient data or in high-risk areas (e.g., dangerous intersections) where caution may lower incidents without eliminating underlying dangers. On the other hand, the proactive approach evaluates intrinsic road safety by analyzing infrastructure, traffic flow, and speed. It is especially valuable where crash data are unavailable or for systematically reducing risks regardless of past incidents. The Network-Wide Assessment (NWA) guidelines [26], introduced by the European Commission in 2023, propose combining these approaches to prioritize interventions. The NWA evaluates road risk using a five-level classification system, integrating crash data (when available) with proactive safety assessments. The process involves verifying the availability of at least three years of reliable crash data, then applying both approaches where data exists or using only the proactive method otherwise, and, finally, creating a risk matrix (see Figure 4) to classify road segments by intervention priority. The integration of the two approaches ensures that critical points identified by the reactive method are automatically assigned a condition of 'Very High Priority', irrespective of the outcomes of proactive assessments. Consequently, the reactive approach was selected as the primary method for identifying high-risk locations within the road network. The  
Figure 3: Flowchart of iRAP methodology. proactive approach, in turn, is reserved for subsequent stages, including validation and cost-benefit analysis. Given sufficient crash data, the reactive approach is recommended for identifying immediate intervention areas. Using GIS tools and heatmaps, high-incident zones are visualized, enabling efficient targeting of problem areas. The Kernel Density Estimator (KDE) is a statistical method used to create heatmaps that visually represent the density of road incidents across specific areas. By analyzing geospatial data, KDE identifies black spots--areas with a high concentration of accidents [27]--guiding the prioritization of safety interventions. To create heatmap, KDE combines individual probability density functions for each data point to generate a smooth visual representation of accident distribution. The bandwidth parameter determines the spread of the density curve, balancing localized detail and broader patterns. Incidents are divided into four categories based on road users involved (i.e., pedestrians, bicycles, motorcycles, and motor vehicles). This disaggregation highlights specific vulnerabilities and tailors interventions to address the needs of each group. Rather than merely evaluating accident rates relative to traffic flow, the analysis prioritizes reducing severe injuries and fatalities. By focusing on the absolute reduction in social costs, interventions target areas with the highest societal impact. After identifying the black spots through reactive analysis by adopting historical crash data to create density maps (heatmaps) that highlight areas with high accident concentrations, they will be classified as high-risk, requiring immediate attention regardless of subsequent evaluations. At this point, the reactive findings should be validated and intrinsic road safety based on infrastructure characteristics, traffic flow, and speed should be evaluated through iRAP star rating, which is a proactive safety evaluation tool. During this process segments are scored from one to five stars for safety based on user categories (drivers, motorcyclists, cyclists, pedestrians). The star rating evaluates over 50 attributes grouped into categories such as road edges, carriageways, intersections, traffic flow, vulnerable user facilities, and speed.