A Novel Methodology for Planning Urban Road Safety Interventions

Abstract

Improving road safety is a major challenge for urban administrations due to the high frequency of accidents and their associated social costs. This study presents a methodology that combines historical accident data analysis with a proactive risk assessment approach to enhance decision-making in road safety planning. Using the International Road Assessment Programme (iRAP) and Geographic Information Systems (GIS), the proposed framework identifies high-risk locations and estimates the benefits of planned safety interventions. A key innovation of this methodology is the integration of cost–benefit analysis to prioritize interventions, ensuring optimal resource allocation. The approach was tested in a medium-sized Italian city where it helped identify critical areas and assess the potential impact of various safety measures, such as intersection redesign and traffic-calming strategies. The results demonstrated a significant potential to reduce accidents and associated social costs, offering a scalable model for urban road safety planning. By integrating data-driven insights with proactive evaluation, this methodology supports urban administrations in implementing effective, targeted interventions that contribute to Vision Zero goals.

1. Introduction

Road accidents remain one of the leading causes of death and disability worldwide, with devastating social and economic impacts. According to the World Health Organization (WHO) [1], approximately 1.19 million fatalities were recorded globally in 2021 due to road incidents, placing immense pressure on healthcare systems and economies. Despite ongoing efforts—such as improving infrastructure, enforcing traffic laws, and launching safety awareness campaigns—the number of accidents remains alarmingly high. Clearly, traditional approaches to road safety need to evolve.

Traditional road safety assessment methodologies can be categorized into reactive, proactive, and predictive approaches. Reactive methods focus on analyzing past accidents to identify high-risk areas, known as black spots, and implement corrective measures accordingly. While these approaches are useful in locations with extensive historical crash data, they fail to address risks in areas where accidents have not yet occurred but where infrastructure deficiencies exist. Proactive methods, on the other hand, assess road safety risks based on design characteristics, traffic flow, and operational speeds, without relying on historical accident data. The International Road Assessment Programme (iRAP) is one of the most widely recognized proactive methodologies, systematically evaluating roads using a standardized star rating system to quantify risks for different types of road users. Lastly, predictive approaches employ statistical models to estimate future accident likelihood, combining historical crash data with advanced mathematical modeling techniques such as Empirical Bayesian estimation, regression models, or machine learning algorithms.

While machine learning-based models and Bayesian Network approaches have recently gained popularity in road safety analysis, they present challenges in terms of interpretability, data requirements, and computational complexity. Bayesian Network Models allow for probabilistic risk assessment by integrating multiple influencing factors, making them effective for identifying accident causation patterns. However, these models often require extensive and high-quality datasets for training, which may not always be available in urban settings. Similarly, machine learning approaches, such as deep learning and random forest classifiers, can analyze vast amounts of data to predict accident likelihood and identify contributing factors. Yet, their “black box” nature and dependency on large, well-labeled datasets make them less practical for immediate implementation by public authorities.

This study adopts the iRAP methodology due to its structured, standardized framework that enables both risk quantification and intervention prioritization. Compared to machine learning and Bayesian methods, iRAP provides a more transparent and interpretable risk assessment process, allowing policymakers to make informed decisions with clear justifications. Furthermore, iRAP integrates both reactive and proactive elements, ensuring a comprehensive approach to road safety evaluation. By combining historical crash data analysis with proactive infrastructure risk assessment, it bridges the gap between traditional black spot identification and forward-looking safety planning.

The main contribution of this study is the development of an integrated methodology that enhances strategic decision-making in road safety planning. By incorporating iRAP risk assessments into a cost–benefit analysis framework, this approach enables urban administrations to prioritize interventions based on both safety impact and economic feasibility. The methodology was tested in a medium-sized Italian city, where it demonstrated significant potential in reducing accidents and optimizing resource allocation. Unlike previous studies that focus solely on reactive black spot identification or predictive modeling, this work proposes a replicable model that balances proactive risk assessment with data-driven intervention planning.

The rest of this paper is structured as follows: Section 2 reviews existing road safety assessment methods, comparing their strengths and weaknesses. Section 3 details our proposed methodology, explaining how reactive and proactive risk assessment can work together. Section 4 presents the case study and its findings and discusses the broader implications of this approach. Finally, Section 5 offers key takeaways and suggestions for future research.

2. Literature Review

Road traffic accidents are a leading cause of death globally, particularly among children and young people aged 5 to 29. According to WHO [1], road crashes accounted for 1.19 million fatalities in 2019, making them the 12th leading cause of death across all age groups. Beyond the tragic loss of life, road accidents impose significant economic burdens, estimated to cost 1–3% of the global Gross Domestic Product (GDP) and up to 6% in some regions. Addressing road safety is therefore not just a public health priority but a critical element of sustainable development, comparable in importance to poverty reduction.

The European Union’s 2021–2030 Strategic Framework for Road Safety [2] highlights the continued severity of traffic accidents, with approximately one million accidents annually causing 23,000 deaths and 120,000 serious injuries. Despite incremental progress, the reduction in road fatalities since 2013 has been insufficient to meet the EU’s target of halving road deaths by 2020. Recognizing this shortfall, the framework introduces the “Safe System” approach, emphasizing safer vehicles, infrastructure, and user behavior, alongside enhanced post-crash care. This approach aligns with the long-term Vision Zero goal of eliminating road fatalities by 2050 and underscores the need for collaborative efforts across governance levels and sectors, with significant political and financial commitments from member states.

In alignment with EU directives, Italy has implemented the National Road Safety Plan 2030 (PNSS 2030) [3], aiming to halve fatalities and serious injuries by 2030 and achieve Vision Zero over the long term. The plan is structured into biennial phases with a monitoring system to assess risk indicators, evaluate intervention impacts, and ensure efficient resource allocation.

Recent statistics [4] confirm the urgency of these measures. In 2023, Italy reported 166,525 accidents, 224,634 injuries, and 3039 fatalities, with over eight deaths daily on average. Vulnerable road users (VRUs), including pedestrians and cyclists, remain at particular risk, with their fatality rates showing little improvement from 2001 to 2023. This highlights the critical need for safer infrastructure, particularly in urban environments, to protect these groups. This body of evidence underscores the importance of innovative, well-funded, and comprehensive strategies to address road safety challenges effectively.

Road accidents are influenced by human behavior, vehicles, and infrastructure [5]. According to the Highway Safety Manual (HSM) [6], human factors play a role in 93% of accidents, highlighting the importance of initiatives like helmet use, driver education, and stricter policies on intoxicated driving. However, these measures have yielded limited success. Vehicle-related factors, accounting for 13% of accidents, have seen improvements due to advancements in automotive safety technologies. Infrastructure, responsible for 34% of accidents, highlights the need for safer road design.

Regarding the latter influencing component, various methodologies such as reactive, proactive, and predictive approaches exist to address road safety.

2.1. Reactive Approaches

Reactive approaches focus on analyzing past accidents to identify high-risk areas (i.e., black spots) in existing road networks, particularly in regions with comprehensive accident data and implement targeted interventions. These methods are less effective in areas with incomplete data, such as developing countries. Reactive approaches can be categorized into aggregate and disaggregate analyses.

• Aggregate analysis provides an overview of road safety by using indicators such as accident frequency, rates, and severity [7] to prioritize interventions. Tools like GIS enhance this process by mapping accident data spatially [8,9], making it easier to identify black spots and plan targeted improvements;
• Disaggregate analysis focuses on specific accident details, such as timing, location, and conditions, to tailor corrective measures. Techniques include collision diagrams, scenario-based analyses, and scenario-class grouping [10], which help identify trends and recommend engineering solutions.

Research shows these methods effectively identify accident patterns and high-risk zones [11]. Tools like the Collision Diagram Builder (CDB) enhance the visualization of accident data over corridors, aiding in the development of strategies to improve road safety [12]. The downside of this method is that it cannot identify risky spots caused by design or infrastructure issues if no accidents have been recorded in those locations.

2.2. Proactive Approaches

Proactive methods assess the inherent risks of road design, focusing on characteristics like geometry, traffic flow, and speed to predict potential fatalities and injuries without relying on historical accident data. A key example is the International Road Assessment Programme (iRAP) [13] which combines proactive and reactive approaches for comprehensive road safety analysis. The iRAP methodology includes five protocols [13]:

• Crash Risk Mapping, a reactive mapping based on historical accident data;
• Star Rating, which proactively evaluates road safety using design parameters;
• Fatalities Estimation Mapping, which estimates potential fatalities and injuries proactively;
• Safer Road Investment Plans (SRIP), which suggests cost-effective safety interventions;
• Performance Tracking, which combines proactive and reactive assessments to prioritize interventions.

Studies have demonstrated the method’s effectiveness. For instance, in Algeria, iRAP and GIS tools identified high-risk segments in a new highway project before it opened, suggesting improvements like speed control [14]. In Romania, an integrated proactive-reactive approach classified road segments by risk levels, addressing infrastructure deficiencies and guiding safety investments [15]. This methodology is adaptable to both new and existing infrastructure, offering a robust framework for road safety planning.

2.3. Predictive Approaches

Predictive methods use statistical models, such as regression analysis, to estimate the likelihood of future road accidents. By combining historical accident data with mathematical modeling, these methods offer a robust foundation for targeted and efficient road safety planning [16]. A key example is the Empirical Bayesian (EB) method, employed by the HSM [6]. The HSM integrates:

• Safety Performance Functions (SPF), which predict expected accidents under base conditions;
• Crash Modification Factors (CMF), which adjust predictions to reflect site-specific conditions;
• Calibration Factors (C_x), which tailor models for different geographic contexts.

This approach identifies high-risk road segments, prioritizes maintenance, and evaluates intervention impacts. Studies in Finland [17] and Norway [18] highlight the accuracy and reliability of EB-based predictions, particularly for planning safety improvements and assessing intervention benefits. Predictive methods provide valuable insights, even with fluctuating accident data, making them a vital tool for proactive road safety management.

2.4. Comparison and Integration of Road Safety Methods

Each road safety analysis method—reactive, proactive, and predictive—has unique strengths that can be leveraged individually or in combination to achieve more accurate identification of high-risk road segments. Integrating these approaches maximizes the effectiveness of safety interventions and resource allocation.

Studies show the value of combining these approaches. For example, integrating statistical models with GIS enhances the spatial accuracy of risk assessments [19,20,21], while hybrid methods balance the strengths of different approaches [22,23]. This synergy enables tailored interventions that address specific network conditions and data availability, ultimately improving road safety management and contributing to sustainable transport systems, as demonstrated in Figure 1.

2.5. Cost–Benefit Analysis

Cost–benefit analysis (CBA) is a vital tool for evaluating the effectiveness of road safety interventions. It quantifies and compares implementation costs with anticipated benefits, particularly in terms of reduced accident rates and severity, ensuring efficient use of public resources.

The social costs of accidents include human costs (loss of life and reduced quality of life), healthcare expenses, productivity losses, administrative costs, and material damages. Regarding the estimation approaches, the Human Capital (HC) and Willingness-to-Pay (WTP) methods are widely used, with WTP generally providing higher and more comprehensive estimates.

Several studies [24,25] highlight disparities in cost estimation across countries due to data quality and methodological differences. They recommend either standardizing international guidelines or adopting flexible approaches tailored to local contexts.

An integrated approach that balances quantitative precision with qualitative considerations enhances the strategic planning of safety interventions, reducing social costs and improving public well-being.

3. Novel Methodology

The methodology proposed in this paper integrates reactive and proactive approaches to support decision-making for urban road safety improvements. This structured process identifies critical areas (black spots) and evaluates risks, enabling optimal resource allocation and prioritization of interventions. The methodological phases include the following:

• The reactive analysis, in which historical accident data are collected, validated, and geolocated to identify high-frequency accident zones—black spots and spatial and statistical analyses provide insights into incident patterns and user behavior (see Figure 2a);
• The proactive assessment based on the iRAP method, which evaluates intrinsic road risks based on geometry, traffic flow, and speed independent of past accident data. This step validates and complements reactive findings, identifying high-risk segments requiring urgent attention (see Figure 2a);
• The intervention design and cost–benefit analysis, in which specific safety solutions are proposed, and their potential impact on reducing fatalities, severe injuries, and social costs is quantified. Implementation costs are estimated, and a benefit–cost ratio is calculated to rank interventions within and across identified sites (see Figure 2b).

By combining reactive data insights with proactive risk evaluation, this methodology provides a comprehensive framework for urban road safety planning, as demonstrated in Figure 2. It equips decision-makers with objective criteria to prioritize actions and achieve sustainable improvements in traffic safety.

While the iRAP star rating methodology is a valuable tool for proactive risk assessment, it has limitations, particularly in complex urban environments. The model is primarily designed for highway and arterial road assessments, which can make it less precise for dense city networks with frequent intersections, varying traffic conditions, and mixed road users. Additionally, iRAP does not account for real-time traffic flow variations or signal timing effects, which are crucial in urban safety assessments. To mitigate these limitations, field observations and complementary GIS analyses were incorporated to refine risk evaluations.

3.1. Reactive Approach to Road Safety

The reactive approach analyzes historical road accident data to identify high-risk areas. By leveraging detailed geolocated incident data, this method provides insights into accident patterns, allowing for targeted safety interventions. The key steps which have been followed are as follows:

• The data collection and validation were performed by providing historical data that was sourced from local or national databases, such as Istat [4] in Italy, which now employs the “GINO” system for facilitated and accurate data reporting. Precise geolocation is critical for identifying black spots. Missing or repeated coordinates are corrected using semi-automated geocoding tools (i.e., API—Application Programming Interface), integrating address information with geographic coordinates;
• The spatial visualization was performed by importing the validated data into Geographic Information Systems (GIS) to create visual layers of accident points. Spatial distribution is analyzed using density maps to identify clusters of incidents (black spots), guiding further risk assessment and safety planning.

This approach emphasizes data accuracy and geolocation precision, ensuring reliable identification of critical areas in the road network for targeted interventions.

3.2. Proactive Approach to Road Safety

The proactive approach focuses on identifying and mitigating road safety risks before accidents occur. Unlike reactive methods that rely on historical crash data, proactive methods assess the intrinsic safety of roads by analyzing geometric features, traffic flow, and speed. This enables the estimation of potential fatalities, serious injuries, and the associated social costs, supporting the design of targeted interventions to improve safety. For this matter, the iRAP framework has been adopted.

The International Road Assessment Program (iRAP) combines five tools (i.e., crash risk mapping, star ratings, FSI, SRIP, and performance tracking) to enhance road safety globally, particularly for VRUs.

The assessment of road characteristics is based on “coding”, which involves analyzing georeferenced images sampled every 100 m along the road segment. These images, combined with traffic flow data and field measurements, form the foundation for safety evaluations. The steps of iRAP methodology are presented in Figure 3.

3.3. 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 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. 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).

$$SRS=\sum Crash Type Scores$$

(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);
• 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).

$$Crash Type Scores=\prod Risk Factors$$

(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. Star rating and SRS (iRAP) [13].

Star Rating	Star Rating Score
	Vehicle Occupants & Motorcyclists	Bicyclists	Pedestrians
			Total	Along	Crossing
5	0 to <2.5	0 to <5	0 to <5	0 to <0.2	0 to <4.8
4	2.5 to <5	5 to <10	5 to <15	0.2 to <1	4.8 to <14
3	5 to <12.5	10 to <30	15 to <40	1 to <7.5	14 to <32.5
2	12.5 to <22.5	30 to <60	40 to <90	7.5 to <15	32.5 to <75
1	22.5+	60+	90+	15+	75+

). 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.

3.4. Cost–Benefit Analysis for Road Safety Interventions

By combining proactive risk evaluation and cost–benefit analysis, it is possible to prioritize road safety measures effectively. By quantifying the social and economic benefits of the proposed interventions, the decision-making process is guided toward solutions that maximize safety while optimizing resource allocation.

3.4.1. Proactive Estimation of Fatalities and Serious Injuries

The proactive cost–benefit analysis estimates reductions in fatalities and serious injuries (FSIs) resulting from road safety interventions. Moreover, it quantifies the associated social cost savings, including human, healthcare, productivity, administrative, and material costs. It considers implementation costs to ensure financial sustainability.

FSIs can be estimated through iRAP’s SRS to predict the annual number of fatalities and serious injuries for four road user categories (drivers, motorcyclists, cyclists, and pedestrians). Calculations incorporate traffic flow (Annual Average Daily Traffic—AADT), crash type risks, and calibration factors tailored to local conditions.

Calibration factors adapt iRAP’s international model to local contexts by integrating historical crash data with proactive risk assessments. Furthermore, it adjusts predictions based on factors like vehicle safety standards and user behavior in specific regions.

One of the novelties of the proposed model is the estimation of serious injury, which relies on a ratio of serious injuries to fatalities (5.5:1 in Italy, based on historical data) to estimate injury figures. In fact, it uses the Maximum Abbreviated Injury Scale (MAIS 3+) to classify serious injuries.

The process described above can be applied to different design solutions, using their respective SRS to assess the associated road safety risks. This approach enables the estimation of the annual number of FSIs after implementing various design alternatives.

For the calculation of the annual number of FSI under the current scenario (Option 0—Do Nothing), the proactive iRAP approach has been chosen. The available Istat dataset provides the number of fatalities for each road segment, but it does not distinguish between minor and serious injuries. Serious injuries are reported only in aggregate for the entire national road network without breakdowns for individual segments. To ensure consistency and comparability of results, the FSI for the “Do Nothing” option is calculated using the same methodology as the other design options, based on risk analysis from the SRS. This ensures that the reactive and proactive approaches remain separate in the cost–benefit analysis, providing a fully proactive calculation independent of historical incident data. The calculation of avoidable FSIs is performed by Equation (3).

$$FS{I}_{saved}=FS{I}_{before}-FS{I}_{after}$$

(3)

where FSI_saved is the estimate of avoidable FSIs per year; FSI_before represents the number of FSIs currently estimated annually; and FSI_after indicates the estimated number of FSIs per year after the implementation of a road improvement intervention.

Estimating the number of avoidable FSI is a critical step in demonstrating the effectiveness of a proposed road safety solution. However, during the initial decision-making phase, particularly in the technical-economic feasibility study, the choice between different design options must also consider the financial resources available. Therefore, evaluating the benefit of an intervention should go beyond just the number of avoided FSIs and include an economic quantification of the results.

3.4.2. Reduction in Social Costs

This approach provides a monetary value for preserving human lives and reducing serious injuries, offering a useful metric for comparing different solutions and justifying investments. Thus, it is recommended to adopt an approach that centers on the reduction in the social costs arising from road accidents as a key criterion in evaluating design alternatives. Social cost is a complex indicator that translates the financial burdens society faces due to accidents, including healthcare, insurance, legal costs, and lost productivity. Specifically, the loss of productivity refers to an individual’s inability to contribute to the country’s GDP following an accident [28].

In Italy, the methodology for calculating the social cost of road accidents was defined by the Ministry of Infrastructure and Transport in 2022 [28] and formalized by the Director’s Decree No. 37 in 2023. To apply these social cost values in the analysis, the most recent data from 2018 [29] were used, indicating the following reference values.

• Social cost per person died in a road accident €1,812,989;
• Social cost per person seriously injured €467,159;
• Social cost per person lightly injured: €8519.

According to Istat [4], the total social cost of road accidents in Italy in 2023 was nearly €18 billion, equivalent to 1% of the national GDP. This underscores the importance of implementing effective road safety policies to alleviate the economic burden on society.

It is important to note that the iRAP model used in the proactive analysis does not account for minor injuries, which were excluded from this study. However, although the social cost of minor injuries is much lower than that of serious injuries, future analyses should consider this category as well, especially since over 200,000 injuries are recorded annually in Italy [4].

The social cost associated with fatalities is calculated for each black spot using Equation (4).

$$S{C}_F=F\times uS{C}_F$$

(4)

where F is estimated annual fatalities (persons); uSC_F represents unitary social cost for each fatality (€/person); and SC_F is total social cost due to fatalities (€). Similar procedures can be followed to calculate the social cost for serious injuries and the total social cost for each road segment is then calculated by summing the costs for fatalities and serious injuries.

This procedure can be applied to both the current conditions of the black spot and the proposed design solutions. The economic benefit of the design alternative is determined by calculating the reduction in annual social costs through Equation (5).

$$S{C}_{saved}=S{C}_{before}\times S{C}_{after}$$

(5)

where SC_saved is annual reduction in social cost; SC_before represents annual social cost under current conditions; and SC_after is annual social cost after road improvement intervention.

3.4.3. Estimation of Intervention Costs

Estimating intervention costs and assessing economic feasibility are essential to ensuring a project’s sustainability and alignment with its objectives, and it can be carried out through a parametric approach during preliminary project phases. The parametric estimation method is particularly effective for evaluating various project options quickly and efficiently. It leverages standardized parameters to estimate intervention costs without requiring detailed quantitative analysis of every component. This allows for a rapid preliminary cost assessment, aiding in selecting options that are both economically feasible and technically sound.

Parametric estimation uses indicators derived from historical data and official price lists to calculate total costs based on project quantities. Common indicators include unit costs per square meter, cubic meter, or installed unit, depending on the type of intervention. Principal cost categories considered in the parametric estimation are as follows:

• Materials and labor, calculated using unit costs applied to projected quantities;
• Site management costs, such as setup, maintenance, and lighting, typically calculated as a percentage of total project costs;
• General project expenses covering design, project management, and other associated costs, estimated at 13–17% of total project costs [30], with an additional 10% for contractor profit [31].

The parametric estimation provides a reliable preliminary cost assessment, ensuring economic viability and supporting informed decision-making during early planning stages.

3.4.4. Prioritization of Interventions

By estimating benefits from reduced social costs of accidents and comparing them with intervention costs, the Benefits/Costs (B/C) ratio serves as a tool for selecting optimal project solutions.

Using the B/C ratio is rooted in the economic concept of opportunity cost, which evaluates the trade-offs of investing limited resources. This method focuses on maximizing safety benefits per unit of expenditure, directing resources toward the most impactful interventions. The B/C ratio is advantageous as it identifies solutions that provide the greatest reduction in accident-related social costs while minimizing missed opportunities.

The decision-making process performs in two stages, as follows:

• Site-specific optimization, in which for each identified critical point, the intervention with the highest B/C ratio is selected as the most cost-effective solution;
• Network-wide prioritization, in which across all critical points, interventions are ranked by their B/C ratios to establish a priority hierarchy, identifying sites with the greatest need for action.

Before implementing prioritized interventions, field inspections are conducted to confirm the necessity of the proposed solutions.

4. Validation of the Proposed Methodology via Case Study Application

To validate the methodology’s effectiveness and usability, the urban road network of a medium-sized Italian city (with approximately 200,000 residents) is selected as a case study, highlighting the benefits of integrating the traditional reactive approach with a proactive approach. The methodology prioritizes the safety of VRUs in urban contexts but is flexible enough to apply to rural scenarios as well.

4.1. 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. To pinpoint high-priority intervention zones, further statistical analysis is required to detect accident hotspots.

4.2. Identification of High-Risk Sites

Pinpointing high-risk sites is essential in road safety management to prioritize interventions and ensure effective corrective actions. This process relies on analyzing historical accident data to identify critical points (reactive) and assessing potential risk conditions of road infrastructure regardless of past incidents (proactive).

As mentioned earlier, the European Commission’s 2023 NWA guidelines [26] introduced a unified methodology for classifying roads into five risk levels. This system combines both approaches, prioritizing intervention for areas flagged as “High Risk” (see Figure 4) by reactive analysis, even if proactive evaluations suggest lower risk.

For the selected case study, with comprehensive accident data available, the reactive approach was prioritized to identify problematic areas and plan immediate interventions. A key tool in this process is concentration maps, which use GIS technology to identify clusters of incidents visually.

Concentration maps, or heatmaps, are GIS tools that visually represent accident density within a specific area. These maps generate a density raster, highlighting zones with frequent incidents using a graduated color scale. Critical areas, or black spots, are visually intensified, guiding planners to high-priority intervention zones.

As a result, four separate concentration maps were created, focusing on incidents involving pedestrians (Figure 5a), cyclists (Figure 5b), motorcyclists (Figure 5c), and vehicles (Figure 5d).

4.3. Proactive Analysis of Black Spots and Intervention Proposals

The proactive approach to road safety assesses and mitigates risks before accidents occur, focusing on the intrinsic safety of road infrastructure. By analyzing physical and geometric characteristics, traffic flows, and operational speeds, it identifies latent risk factors and quantifies potential improvements through corrective measures. This complements reactive methods, which rely on historical accident data.

High-risk sites identified through reactive analysis underwent a proactive evaluation using the iRAP star rating methodology and the ViDA software (2024 version). The process involved a detailed “coding” of the road characteristics based on satellite imagery, traffic data, and field measurements. This yielded star ratings and star rating scores, quantifying risks at each site. Proposed interventions were then evaluated proactively, simulating their impact on safety metrics to estimate reductions in fatalities and serious injuries.

Below is the application of the described procedure to some of the black spots in the selected road network, highlighting both the adaptability and the limitations of the proactive method in road safety evaluations.

4.3.1. Black Spots for Pedestrians

According to the concentration map, critical point A is selected regarding an incident involving pedestrian crossing (see Figure 6).

A pedestrian crossing is a critical site for urban road safety. It features two lanes per direction and high traffic volumes—6400 vehicles daily westbound and 18,400 eastbound (January–March 2024 data). This configuration poses significant risks: pedestrians crossing may be hidden by stopped vehicles, particularly in the faster “passing” lane, creating blind spots for oncoming drivers.

Between 2017 and 2023, the crossing witnessed seven pedestrian-related incidents, including one fatality, averaging one incident per year. The current geometric features and traffic flows were input into the iRAP star rating model, assuming an operating speed of 50 km/h. Below are the results for the current state (Option 0) of the analyzed road segment for both traffic directions.

The proactive analysis confirmed the safety issues for pedestrians, immediately revealing a strong correlation between the risk level and traffic intensity. As shown in Figure 7a,b, the star rating for pedestrians is particularly critical in the eastbound direction, where the average daily traffic is three times higher than in the opposite direction, and the risk score is eight times greater.

Two potential interventions were proposed to improve pedestrian safety at this critical crossing. The first option focuses on installing a pedestrian traffic light, providing a localized and immediately effective solution. Additionally, implementing advanced technologies, such as pedestrian countdown timers or automatic detection systems, could be considered in subsequent phases. Countdown timers enhance crossing safety by informing pedestrians of the time left to complete their crossing. This allows pedestrians to make better decisions based on their walking speed and reduces unsafe behaviors by clearly indicating waiting times for the green light phase.

The proactive analysis of this intervention (Option 1) showed a significant reduction in pedestrian risk levels, particularly in the eastbound direction, with the SRS decreased by a factor of eleven, as demonstrated in Figure 8.

The second intervention proposes a broader reorganization of the northern side of the avenue, focusing on reducing the number of westbound lanes from two to one between the roundabout and the bus stop. Peak westbound traffic volumes of approximately 600 vehicles per hour suggest that a single lane can accommodate the flow, as traffic already merges into one lane upon exiting the roundabout. The second lane, present immediately after the roundabout, may encourage unsafe behaviors such as speeding or overtaking vehicles that are stopped for pedestrians.

The proposed removal of the second lane could create space to widen the sidewalk, enhancing pedestrian and cyclist mobility on the northern side. While this modification does not directly address the crossing’s safety, it improves overall transit quality. Conversely, eastbound traffic volumes are too high to implement a similar lane reduction.

A simulation of the single-lane configuration westbound (Option 2) assessed the SRS for both directions (see Figure 9). Although the intervention primarily targets westbound traffic, its positive impact extends to the eastbound direction, which faces higher traffic volumes and associated risks. Introducing the “2 + 1” lane attribute in the model reduced the SRS for the eastbound direction by approximately four times, reflecting a notable improvement in safety conditions.

Another selected black spot is critical point B, identified through heatmaps (see Figure 10a), which includes two pedestrian crossings, approximately 80 m apart. Traffic volume data from the municipality indicates an average daily traffic (ADT) of 12,500 vehicles westbound (toward the city center) and 7900 vehicles eastbound (leaving the city).

Between 2017 and 2023, 10 pedestrian-related accidents occurred at this location, including one fatality. Based on accident geolocation, the crossings were analyzed separately as critical point B1 with pedestrian crossing and B2 with pedestrian crossing adjacent to bus stops in both directions (see Figure 10b).

Both crossings share identical geometric and traffic flow characteristics, with a road width of approximately 9 m, two 4 m-wide lanes, and 0.5 m shoulders. However, B2 is treated separately due to the influence of nearby bus stops. This stretch of road is classified as E2 (secondary neighborhood street), where the wide lanes encourage speeding, posing significant risks to vulnerable road users.

Given their similarities, a single proactive simulation was conducted using the iRAP star rating model for the current state (Option 0) at both crossings. With an operational speed of 50 km/h for both directions, the results revealed a higher pedestrian risk score for westbound traffic, nearly double that of the eastbound direction, reflecting the differing traffic volumes (see Figure 11).

To reduce operational speeds along the identified stretch, two traffic calming measures were proposed. The first intervention option proposes a pedestrian crossing with a hook-shaped refuge island at point B1 (see Figure 12a) and a central anti-overtaking refuge island near the bus stop at point B2 (see Figure 12b).

This solution involves the addition of pedestrian refuge islands to narrow lane widths, thereby reducing drivers’ visual space and encouraging lower speeds. In detail, at point B1, a 2 m-wide hook-shaped pedestrian island would enhance safety and also support bicycle crossings by optimizing available space. At point B2, near the bus stop, a central refuge island would function as both a pedestrian safety feature and an anti-overtaking barrier.

These interventions aim to induce slight deviations in vehicle trajectories, effectively calming traffic. The measures include adequate visibility and signage enhancements for day and night conditions. Proactive risk analysis for this option, assuming a reduction in operating speed from 50 km/h to 35 km/h, showed significant improvements in safety (see Figure 13).

An alternative intervention involves the installation of “Berlin cushions” before the pedestrian crossings. This solution is favored over speed bumps as it does not disrupt public transport operations, such as bus routes (see Figure 14).

Assuming a reduced crossing speed of 30 km/h, the proactive risk analysis for this speed management solution demonstrated notable safety improvements (see Figure 15).

While the risk reduction is less pronounced than that achieved with signalized crossings, the intervention still provides a meaningful enhancement to road safety.

4.3.2. Black Spot for Cyclists

The second black spot (C) highlights a dangerous cyclist crossing at a square (see Figure 16).

The crossing spans 18 m, traversing two lanes in each direction, separated by a narrow central island under two meters wide. Although equipped with a non-active traffic light, the location handles an average daily traffic of 10,000 vehicles in both directions, as per Origin–Destination (O-D) matrix data from the municipality. Between 2017 and 2023, 15 cyclist-related accidents, including one fatality, occurred near this crossing, underscoring its high risk.

Despite entering the crossing’s characteristics into the iRAP model, including a 40 km/h operational speed and the presence of a bike lane, the simulated star rating produced a negligible risk score (see Figure 17). This noticeably contrasted with the actual accident data, prompting further investigation into the model’s limitations regarding cyclist crossings.

The iRAP ViDA platform evaluates VRUs with attributes like pedestrian crossings and cycling infrastructure. However, it lacks specific metrics for cyclist crossings, a critical gap in assessing cycling safety. As a workaround, the analysis attempted to use pedestrian crossing ratings for cyclist crossings, as pedestrian evaluations often reflect crossing-related risks more accurately.

This adaptation, however, introduced distortions in estimating fatalities and serious injuries. Consequently, critical point C was excluded from the proactive cost–benefit analysis. The study emphasizes the need for iRAP to refine its methodology to better address the safety of cyclist crossings, considering both the quality of cycling infrastructure and crossing safety.

4.3.3. Black Spot with Motorized Traffic

Two critical locations (D and E) for motorized traffic incidents were identified, as demonstrated in Figure 18.

The first one is critical point D, which is the roundabout is notable for high accident density involving motorcycles and automobiles. This complex roundabout consists of five exits, four entries, and three circulating lanes. With peak hourly traffic flows of 4500 vehicles in the morning and 4000 in the evening, its multi-lane configuration encourages risky lane changes and high-speed driving.

Proximity between entries and exits reduces maneuvering space, often resulting in collisions during entry and exit maneuvers. The accident distribution across the roundabout was not localized, highlighting a systemic issue rather than a single entry or exit causing the incidents (see Figure 19).

The second one is critical point E, which is a signalized intersection, characterized by frequent vehicle collisions due to red-light violations. This signalized intersection reported 11 vehicle collisions from 2017 to 2023, predominantly caused by vehicles running red lights, conflicting with traffic from the northern side (see Figure 20).

The iRAP methodology has notable shortcomings in evaluating intersections, particularly in complex or signalized configurations due to the following:

• Simplistic modeling: Intersections are treated as extensions of road segments rather than standalone entities, limiting detail on entry and exit flows;
• Traffic aggregation: iRAP aggregates traffic volumes without accounting for direction-specific flows, reducing risk analysis precision;
• Signal timing ignored: Signal cycle durations, critical to traffic flow and conflict prevention, are not considered;
• Uniform parameters: Roundabouts are evaluated using standardized attributes that fail to reflect detailed factors like lane config., entry angles, or geometric design.

Due to these limitations, critical points D and E were excluded from the proactive cost–benefit analysis.

4.4. Cost–Benefit Analysis of Safety Interventions in Parma

A proactive CBA was conducted to evaluate proposed safety interventions for critical road segments in Parma. The analysis aims to quantify potential reductions in fatalities and serious injuries (FSI), as well as the associated decrease in societal costs, including healthcare expenses, legal fees, and productivity losses. Simultaneously, the financial feasibility of each intervention was assessed to ensure sustainable resource allocation.

4.4.1. Critical Point A

The iRAP model was used to estimate the potential annual reduction in fatalities and serious injuries for two intervention options at critical point A. The measure of FSI_saved highlights the risk reduction compared to doing nothing (Opt.0), considering traffic volumes in each direction (see

Table 2. FSI saved for the east and west directions.

[n°/Year]	Fatalities		Serious Injuries		FSI		FSIsaved
	East	West	East	West	East	West	East	West
Opt.0	0.402	0.026	2.213	0.147	2.615	0.174	-	-
Opt.1	0.072	0.015	0.398	0.087	0.471	0.102	2.144	0.071
Opt.2	0.137	0.022	0.754	0.126	0.891	0.148	1.724	0.025

).

For each proposed intervention, the reduction in social cost (SC) was estimated using unit costs established in Italy for fatalities–F (€1,812,989.00) and serious injuries–SI (€467,159.00) in road accidents. The total benefit of these interventions was calculated by summing the benefits across both traffic directions (see

Table 3. SC saved for the east and west directions.

[€/Year]	SCF		SCSI		SCTOT		SCsaved
	East	West	East	West	East	West	East	West
Opt.0	729,564.60	48,719.40	1,033,941.56	69,045.30	1,763,506.17	117,764.70	-	-
Opt.1	131,470.79	28,715.37	186,320.87	40,695.53	317,791.66	69,410.90	1,445,714.50	48,353.80
Opt.2	248,686.54	41,534.12	352,439.45	58,862.30	601,125.99	100,396.43	1,162,380.18	17,368.27

).

The intervention costs were estimated (see

Table 4. Cost estimation of proposed interventions.

Intervention Option (1)	Unitary Cost [€]	Unit	Quantity	Cost [€]
Traffic light system	16,253.20	each	1	16,253.20
Excavation for laying the piles	79.05	m	2	158.10
Horizontal road markings	6.93	m2	6	41.58
Total				16,452.88
Real cost, VAT included (×1.35)				22,211.39
Intervention option (2)	Unitary Cost [€]	Unit	Quantity	Cost [€]
Partial demolition of the pavement	5.30	m2	90	477.00
Removing the curbs	4.25	m	60	255.00
Excavation	3.99	m2	90	359.10
Subgrade preparation	0.84	m2	90	75.60
Paving	23.80	m2	90	2142.00
Curbs	18.92	m	60	1135.20
Horizontal road markings	1.00	m	120	120.00
Drainage channel	36.48	m	60	2188.80
Total				6752.70
Real cost, VAT included (×1.35)				9116.14

) using the latest regional price list for public works and market research for specific traffic-calming elements. For narrowing the carriageway from two lanes to one, the project includes a 1.5 m widening of the sidewalk over a 60 m stretch.

The cost determined in this manner (for both critical points A and B) represents only the construction cost within the economic framework. However, it excludes the additional funds that are part of the real cost. Among the main components of the additional funds are technical expenses, which, typically in Italy, range between 12 and 15% of the construction cost, as well as VAT (in Italy is equal to 22%). Therefore, a multiplicative factor (1.35) is introduced to account for ‘other costs, VAT included’, which provides an accurate estimate of the total cost in the economic framework (real cost). Since a CBA (cost–benefit analysis) methodology is being proposed, it is essential that costs and benefits are fully comparable (homogeneous).

4.4.2. Critical Point B

Two pedestrian crossings (B1 and B2) were analyzed for safety improvements. Two traffic calming proposals were evaluated: (1) installing refuge and anti-overtaking islands and (2) adding speed cushions. The iRAP model estimated the number of fatalities and serious injuries preventable by these measures, which could also be integrated for combined benefits (see

Table 5. FSI saved for the east and west directions.

[n°/Year]	Fatalities		Serious Injuries		FSI		FSIsaved
	East	West	East	West	East	West	East	West
Opt.0	0.086	0.211	0.476	1.162	0.563	1.373	-	-
Opt.1	0.065	0.152	0.359	0.837	0.424	0.989	0.139	0.384
Opt.2	0.075	0.180	0.415	0.993	0.490	1.173	0.072	0.199

).

Again, for each proposed intervention, the reduction in social cost (SC) was estimated, as presented in

Table 6. SC saved for the east and west directions.

[€/Year]	SCF		SCSI		SCTOT		SCsaved
	East	West	East	West	East	West	East	West
Opt.0	157,147.48	383,106.33	222,709.97	542,939.66	379,857.45	926,045.99	-	-
Opt.1	118,373.80	275,966.24	167,759.77	391,100.34	286,133.57	667,066.58	93,723.88	258,979.40
Opt.2	136,840.04	327,448.32	193,930.19	464,060.93	330,770.22	791,509.25	49,087.22	134,536.74

.

Preliminary cost estimates were conducted for two proposed traffic-calming solutions at critical points B1, and B2. Option 1 includes central pedestrian refuge islands, while Option 2 involves Berlin cushions to reduce vehicle speed (see

Table 7. Cost estimation of proposed interventions.

Intervention Option (1)	Unitary Cost [€]	Unit	Quantity	Cost [€]
Hook-shaped refuge island
Paving	23.80	m2	8	190.40
Island curbs	18.92	m	16	302.72
Vertical obstacle warning sign	39.55	each	2	79.10
Horizontal zebra stripes	6.93	m2	4	27.72
Pedestrian path delimitation Curbs	18.92	m	12	227.04
Pedestrian crossing	6.93	m2	10	69.30
Sub-total				896.28
Bus stops, with central island
Paving	23.80	m2	60	1428.00
Island curbs	18.92	m	120	2270.40
Vertical obstacle warning sign	39.55	each	4	158.20
Horizontal zebra stripes	6.93	m2	10	69.30
Pedestrian crossing	6.93	m2	16	110.88
Sub-total				4036.78
Total				4933.06
Real cost, VAT included (×1.35)				6659.63
Intervention option (2)	Unitary Cost [€]	Unit	Quantity	Cost [€]
Recycled rubber prefabricated unit	500.00	each	4	2000.00
Installation	200.00	each	4	800.00
Total				2800.00
Real cost, VAT included (×1.35)				3780.00

). Both interventions aim to enhance pedestrian and cyclist safety.

4.5. Intervention Prioritization

Intervention priorities were determined through a cost–benefit ratio (B/C) analysis of the proposed solutions. By evaluating the social cost reduction benefits of reducing accidents alongside the costs of implementation, the B/C ratio provided a robust criterion for selecting the most effective interventions.

The B/C ratio allows for the comparison of various proposals to identify those that maximize benefits relative to costs, focusing resources on measures that have the greatest impact on reducing accident-related social costs. The decision-making process consists of two phases.

In phase 1, for each critical site, the intervention proposal with the highest B/C ratio is selected as the most efficient solution (see

Table 8. B/C ratio for critical points A and B.

Critical Point A				Critical Point B
[€/Year]	Benefit SCsaved	Int. Cost	B/C	[€/Year]	Benefit SCsaved	Int. Cost	B/C
Option 1	1,494,068.31	22,211.39	67.26	Option 1	352,703.28	6659.63	52.96
Option 2	1,179,748.45	9116.14	129.41	Option 2	183,623.96	3780.00	48.57

).

As demonstrated in the table, at critical point A, Option 2—narrowing to one westbound lane and expanding the sidewalk—emerged as the optimal choice.

At critical point B, Option 1, which involves installing refuge islands, was prioritized over the Berlin cushions.

During phase 2, a network-wide evaluation of critical points ranks sites based on their B/C ratios (see

Table 9. Prioritization of interventions, network wide.

[€/Year]	Priority Intervention	Benefit SCsaved	Int. Cost	B/C
Critical point A	Option 2	1,179,748.45	9116.14	129.41
Critical point B	Option 1	352,703.28	6659.63	52.96

).

As it is demonstrated in the table, between points A and B, critical point A was identified as the highest-priority intervention. Before implementation, on-site inspections are recommended to confirm the necessity and feasibility of the proposed measures.

5. Conclusions

Road safety remains a critical priority for urban administrations, requiring data-driven strategies to reduce accidents and optimize resource allocation. This study introduced an integrated methodology that combines reactive accident analysis with proactive risk assessment, leveraging the iRAP star rating system and Geographic Information Systems (GIS) to improve road safety planning. By incorporating cost–benefit analysis (CBA), this approach not only identifies high-risk areas but also prioritizes interventions based on their expected safety benefits and economic feasibility.

A key contribution of this research is its ability to bridge the gap between historical crash data analysis and proactive risk evaluation, offering a more comprehensive decision-making framework than traditional black-spot identification methods. Unlike previous studies that rely solely on past accident records or predictive models, this methodology proactively addresses infrastructure-related risks, even in locations with limited historical data. Additionally, the integration of CBA within the iRAP framework represents a novel advancement, ensuring that safety investments are both impactful and cost-efficient.

Despite its strengths, integrating reactive and proactive approaches posed challenges. Methodological conflicts arose in cases where historical crash data did not align with proactive risk assessments—for example, some locations flagged as high-risk by iRAP had relatively low recorded accident frequencies. To settle these differences, field inspections and local traffic flow analyses were conducted, ensuring that interventions addressed both recorded incidents and underlying infrastructure risks. Another challenge involved data compatibility, as municipal and ISTAT datasets exhibited discrepancies in accident reporting. These were mitigated through geolocation standardization and cross-verification to improve data reliability.

This study supports Vision Zero goals by providing a proactive, data-driven framework that identifies risk areas before severe crashes occur. Unlike traditional reactive approaches that focus solely on black spots, the integrated methodology prioritizes preventative measures, such as redesigning roads to inherently reduce conflict points and crash severity. The inclusion of CBA further strengthens Vision Zero efforts by ensuring cost-effective interventions that maximize safety gains.

The proposed methodology aligns with both Italian and EU road safety policies, particularly EU’s Strategic Road Safety Action Plan (2021–2030), which promotes data-driven risk assessment and network-wide road safety evaluations, and Italy’s National Road Safety Plan (PNSS 2030), which prioritizes high-risk site interventions and encourages cost-effective infrastructure improvements.

The study’s integration of iRAP risk assessments and GIS-based black spot analysis is directly compatible with the EU’s Network-Wide Road Safety Assessment methodology (2023), ensuring policy coherence and practical applicability across European cities.

Given limited public budgets, local governments can prioritize interventions using a layered approach. For instance, the following can be considered:

• High-impact, low-cost measures first—solutions like traffic signal upgrades, pedestrian refuge islands, and speed management (e.g., Berlin cushions) should be implemented as they offer quick safety benefits with minimal investment;
• Data-driven prioritization—cost–benefit analysis (CBA) helps rank interventions by maximizing accident reduction per money spent, ensuring resources are directed toward the most cost-effective solutions;
• Phased infrastructure upgrades—larger interventions (e.g., road redesigns) can be implemented in stages to align with budget cycles and political feasibility;
• Leveraging external funding—governments can seek EU road safety grants and public–private partnerships to finance larger-scale projects.

Future research could explore refining risk models for complex urban intersections, where iRAP’s current methodology has limitations, and further validate the approach in larger metropolitan areas with diverse traffic conditions. Expanding the integration of machine learning techniques within this framework could also enhance predictive capabilities, providing even stronger decision-making tools for urban planners and policymakers.