Large Language Models (LLMs) and vision-language models can deliver smart mobility solutions in Africa in order to address the region’s unique transportation challenges. On 29 April 2025, Dr. Ahmed Biyabani, Associate Teaching Professor at Carnegie Mellon University Africa, with a specialization in AI and digital transformation, led an AI for Good webinar exploring this topic. The webinar explored how LLMs can process various data sources such as GPS, images and time-series data, in order to predict congestion and optimize traffic flows. It highlighted the value of employing domain-adapted models trained on mobility-specific datasets from African cities, arguing that while general purpose LLMs perform well in broad tasks, they often require domain specific expertise in order to be applied in transport-related scenarios.
In order to effectively address this issue, the webinar explored how retrieval augmented generation can be used to improve LLM performance. Dr. Biyabani noted that this could be done by integrating real-time, localized data such as GPS, schedules, weather patterns, infrastructure maps and traffic warnings to assist in contextual decision-making. The webinar incorporated case studies from Kigali, Nairobi and Lagos to demonstrate how local data can improve model accuracy. It also proposed a three-stage system architecture - namely data collection (i.e curating Africa-specific datasets), model adaptation (i.e fine-tuning vision-language models and deploying retrieval-augmented generation for dynamic updates) and deployment (i.e a web-based application for real-time mobility and insights).
This framework provides the ability to improve traffic management, emergency response and public transportation services in low-resource settings. The webinar additionally explored issues such as computational resource requirements, data privacy, bias reduction and human oversight, in addition to emphasizing the need for access to high-quality, relevant data. It noted that this framework can be applied to sectors beyond transportation, such as healthcare and logistics, therefore demonstrating the applicability of multimodal AI in emerging economies. The webinar concluded with a call towards more collaborative data-sharing initiatives and the need to develop supportive policy frameworks which can help grow innovative solutions across the African continent.
This case study is an excerpt from the AI for Good flagship report produced by UNU-CPR, Unlocking AI's Potential to Serve Humanity.
