Urban traffic congestion in African cities: A mathematical review

Accelerated urbanisation, poor infrastructure, private vehicle ownership and land-use planning have made traffic congestion a significant challenge confronting African cities. Beyond creating daily delays, congestion hampers economic productivity, increases the risk of infrastructure projects, and compromises environmental sustainability. This paper adopts a systematic mathematical review approach to show how mathematical modelling has a solid arsenal for diagnosing and addressing these challenges. Techniques such as network theory, agent-based models (ABMs), and epidemic-inspired Susceptible–Infected–Recovered (SIR) models have been found to model the propagation of traffic congestion in urban networks, even with sparse data. Applications in some African cities, such as Lagos, Dar es Salaam, Cai and Johannesburg, demonstrate how these models can identify systemic bottlenecks, estimate tipping points, and assess resilience based on various policies or infrastructure options. Significantly, SIR-inspired models enable the calibration of epidemiological parameters, such as the basic reproduction number, to quantify under what conditions traffic congestion spreads or dies out. The article continues to discuss the potential application of hybrid models that integrate mathematical models with emerging real-time data sources, such as mobile phones and GPS data, to enhance predictive accuracy. These models not only provide understanding of short-term congestion behaviour but also inform long-term planning, allowing policymakers to align transport strategies with sustainable development goals like infrastructure delays and cost overruns. By building integrated, evidence-based modelling techniques, this research reviews and shows future direction in the building of adaptable and context-specific transport models that can respond to the unique realities of Africa’s rapidly urbanising cities.