<p>From Scats to Mobility Justice: An AI-Driven Traffic Control Standard for Heterogeneous African Cities</p>

Rapid urbanization in Africa has intensified a mobility crisis, with traffic congestion contributing to economic losses, public safety challenges, and environmental degradation. Many adaptive traffic control systems are designed for regulated, lane-based environments and homogeneous traffic patterns, limiting their applicability in heterogeneous urban contexts. This paper examines the Sydney Coordinated Adaptive Traffic System (SCATS) as a reference model to assess its suitability for Sub-Saharan African contexts. While SCATS demonstrates reductions in travel time, stops, fuel consumption, and emissions through real-time signal optimization, its vehicle-centric design and reliance on fixed infrastructure risk worsening existing inequalities when directly deployed. Drawing on these insights, the paper proposes a mobility justice framework to inform an AI-driven traffic control standard for heterogeneous and resource-constrained urban environments. The framework outlines standardization-relevant design principles, including multi-source AI-based detection to replace inductive loops, priority-based algorithms for public and informal transport, and modular, solar-powered infrastructure. By reframing traffic control standardization from vehicle throughput to multimodal equity, this paper contributes a policy-oriented perspective on how adaptive traffic technologies can be responsibly deployed, ethically governed, and integrated into future international standards for sustainable and inclusive urban mobility.