Abstract:
This study presents a comprehensive framework for the application of Artificial Intelligence (AI) technologies in traffic management in Rwanda. The research was driven by growing interest in leveraging AI to address persistent traffic-related challenges such as congestion, inefficient signal control and limited real-time decision-making. Data was collected through structured questionnaires and semi-structured interviews to assess the level of awareness, current adoption, perceived challenges and potential strategies related to AI integration in the Rwandan transport sector. The findings revealed a generally low level of awareness and adoption of AI technologies, across various tools such as machine learning, computer vision and natural language processing. Interviews with key stakeholders highlighted infrastructural limitations, lack of technical capacity and absence of a clear policy framework as significant barriers to implementation. Based on these findings, a context-specific framework was developed, focusing on four strategic pillars: awareness and education, capacity building, policy and regulatory support and infrastructure development. The proposed framework aims to guide policymakers, transport authorities and technology stakeholders in a systematically integrating AI into traffic management in a scalable, inclusive and sustainable manner. This study contributes to the growing body of knowledge on AI in smart mobility and offers actionable insights for enhancing urban transport systems in Rwanda.
