A workflow to promote the analysis of smart card data - a case study of Rea Vaya

Abstract

Automatic Fare Collection (AFC) systems in public transport generate large volumes of smart card data (SCD) that can support evidence-based planning and policy-making. However, public transport operators in developing countries frequently fail to extract and analyse SCD, foregoing potential productivity improvements and strategic decision support. Addressing this requires understanding the institutional, technical, and resource constraints in developing contexts. This paper has two aims: to explore factors contributing to poor SCD use, and to develop a context-sensitive workflow solution for SCD analysis requiring minimal additional resources. Taking a grounded approach, we use Johannesburg's Rea Vaya Bus Rapid Transit system as a case study. Focus groups explored constraints transport officials face in extracting value from SCD. Management unawareness, skills shortages, and data quality problems create a self-perpetuating cycle of limited data use. From a technology adoption perspective, this demonstrates how data-intensive innovations diffuse unevenly when organisational readiness, governance frameworks, and skills development lag behind technological deployment. We conducted prototype analyses of SCD and General Transit Feed Specification data to understand data quality issues. A staged workflow solution is proposed: an immediately implementable "quick win" using open-source tools, and a longer-term scalable architecture. Results demonstrate that meaningful operational and planning indicators—including origin-destination matrices, passenger demand profiles, waiting times, and journey time metrics—can be derived with modest investment. The findings illustrate that minimal organisational innovation is required for realising sustainable data-driven decision making in public transport systems in the global South.