Artificial Intelligence for Urban Safety: A Case Study for reducing road accident in Genoa

Abstract. This study explores the application of Machine Learning (ML) and citizen engagement in improving road safety for vulnerable populations (pedestrians, cyclists) in Genoa, Italy. Aligned with the UN's 2030 Agenda for Sustainable Development, the project aims for a 50% reduction in traffic accidents by 2030.The AI4PublicPolicy initiative introduces the Virtual Policy Management Environment (VPME) platform. VPME utilizes ML, Deep Learning (DL), Natural Language Processing (NLP), and chatbots to empower the policy development lifecycle. Citizen feedback is integrated through workshops and surveys, fostering a citizen-centric approach. The Genoa pilot program demonstrates VPME's capabilities. ML models analyse historical accident and Geographic Information Systems (GIS) data to predict future high-risk areas. These predictions inform resource allocation and targeted interventions for pedestrian crossings and school walking routes ("Pedibus"). Dashboards visualize the model outputs, allowing users to assess risk levels and predict accident occurrences. Future improvements include incorporating additional data sources (demographics, real-time traffic) for enhanced model accuracy. Citizen engagement played a vital role. Co-creation workshops facilitated stakeholder participation in defining Use Cases, User Stories, and project objectives. Discussions focused on integrating data from environmental, traffic, and citizen reporting systems with VPME solutions. Participants evaluated the project approach and provided valuable feedback. The project highlights the potential of AI and citizen collaboration for data-driven policymaking. This approach empowers municipalities to make informed decisions that prioritize public safety and well-being.