Improving Urban Road Safety: A Data-Driven Approach combining Analytical and Simulation Models for Effective Traffic Calming Interventions

Road safety in urban areas is a key challenge for sustainable mobility and quality of life. Pedestrians are vulnerable to risks from both vehicle traffic and unsafe behaviour. The use of smartphones, whether while driving or crossing the road, has introduced new forms of distraction that impair attention and increase the risk of collisions. In addition, non-compliance with traffic rules, such as speeding limits and running red lights, contributes to an unsafe urban environment. Advanced Traffic Management Systems (ATMS), including Red-Light Enforcement (RLE) and Automated Speed Enforcement (ASE) systems, aim to reduce collisions by encouraging safer driving. However, limited resources make it essential to strategically place these systems for maximum effectiveness. This study presents a data-driven approach that integrates real-world data with analytical and simulation models to assess the probability of collisions and analyze the effects of the placement of these systems. The proposed methodology allows to evaluate traffic calming measures and their impact. Using fixed sensors to collect vehicle speeds and gap distances, we performed statistical analyses to quantify speed reductions and assess the risks for pedestrian safety by calculating the Collision Probability (CP) using a calibrated microsimulation model. The methodology has been tested in Catania (Italy), where recent investments have enabled the installation of these AMTS and RLE systems. The results demonstrate a significant reduction in CP: from 10.24% to 7.78% in the 5:00–6:00 time slot and from 36.89% to 27.86% in the 8:00–9:00 slot. This corresponds to a relative decrease in CP of 24% and 25%, respectively. These findings highlight the potential of the proposed tool to support administrations in evaluating traffic interventions, with scalable applicability to similar urban contexts and tangible benefits for both policymaking and pedestrian safety.