# A Novel Methodology for Planning Urban Road Safety Interventions
## 4 Validation of the Proposed Methodology via Case Study Application
### Accident Data Collection, Validation, and Analysis  
The municipal road network of the selected city is diverse and includes various road types. This network comprises major roads, such as motorways, along with four surrounding external bypass roads, which are primarily designed for motorized traffic and exclude pedestrians and cyclists [32]. Since the analysis focuses on urban safety, accidents on these roads were excluded, emphasizing incidents occurring within the city, where mixed modes of transportation--cars, bicycles, and pedestrians--make safety management more complex. The first step in identifying critical areas involves analyzing historical road accident data. For this matter a sufficiently long analysis period is essential to account for the random nature of accidents and accurately identify high-risk locations. For this case study, accident data from 2017 to 2023 were analyzed, sourced from the following:  
* The municipality (2017-2022), 4971 accidents recorded by law enforcement;
* The Istat (2017-2022), 4587 accidents processed by provincial and regional statistical offices before inclusion in national databases;
* The municipality (2023), 862 accidents recorded using a new management system introduced by the municipality. The reactive analysis method heavily depends on the accuracy of data, particularly geolocation information. Since this study relies on multiple datasets, it was essential to account for potential discrepancies and biases between these sources. Differences can arise due to variations in reporting standards, data collection methods, and levels of completeness. Therefore, before analysis, all records undergo validation to address common issues such as missing or duplicate geographic coordinates. In this regard, data entries were categorized as follows:  
* Unique coordinates: directly usable;
* Duplicate coordinates: indicative of potential data errors;
* Missing coordinates: requiring recovery. Geolocation validation was conducted using a semi-automated geocoding system, which converted address descriptions into latitude and longitude coordinates. Manual verification was applied to ensure precision. Records lacking both coordinates and address details were deemed unusable. Valid and recovered records formed the final dataset for accident heatmap analysis. Out of 4971 accident records from the municipality dataset (2017-2022), 56% contained duplicate coordinates and lacked sufficient address information, leaving only 44% (2186 records) valid for analysis. The Istat dataset provided a more robust foundation, with 69% of the 4587 records containing unique coordinates. Additionally, geocoding based on street names enabled recovery of another 783 records, resulting in 3934 validated accidents (86% of the dataset). Given this higher data quality, the Istat dataset was selected for analysis over the municipal dataset. Applying the same validation process to the 2023 municipal dataset, 88% of the 862 records contained unique coordinates. Geocoding of street names further improved coverage, yielding 851 validated accidents (99% of the dataset). Finally, these datasets were unified to create a cohesive dataset spanning 2017-2023. The final unified dataset consisted of 4785 validated and geolocated accidents, representing 88% of the total available records. After excluding incidents on highways and bypass roads, the dataset focused exclusively on the urban context, resulting in 4288 validated records (79% of total incidents). The validated 2017-2023 dataset was integrated into a GIS platform, creating a vector layer of points representing individual accidents. These points were mapped and categorized into four groups of pedestrian-related accidents (626 incidents), bicycle-related accidents (1116 incidents), motorcycle-related accidents (1066 incidents), and car-only accidents (1630 incidents). While this point-based visualization provides a clear spatial representation of the incidents, it is insufficient to identify areas with the highest accident concentrations effectively.