# 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. road safety; road accidents; black spots; vulnerable road users (VRUs); urban infrastructure; iRAP methodology; geographic information systems; cost-benefit analysis 2020 IEEE/RSJ International Conference on Intelligent Transportation Systems (ICRI), 2020  
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.