J.T. Sello, MJ BOOYSEN
Paratransit is the dominant mode of public transport in much of sub-Saharan Africa, yet its electrification is complicated by decentralised operations, adaptive driver behaviour, and limited charging infrastructure. This paper presents a modular agent-based simulation framework for electric minibus taxis that explicitly represents driver route, stopping, and charging decisions using discrete choice and opportunity-cost concepts. The model is calibrated and validated with empirical GPS-based operating data from Stellenbosch, South Africa, and applied to scenarios spanning different electric vehicle adoption levels, depot connector counts, charging power ratings, and home-charging availability. The results show that constrained depot charging leads to longer passenger waits and fewer trips being served, particularly at high electric vehicle penetration. By contrast, home charging consistently reduces depot congestion and helps preserve service quality, even at high adoption levels. Increasing connector power improves outcomes, but gains become limited beyond 22 kW per connector. Charging strategy also strongly shapes grid demand: depot-only charging concentrates load during operating hours, whereas home charging shifts demand to evening residential peaks, indicating a need for managed charging. Emissions reductions are achievable, but their magnitude depends strongly on grid carbon intensity. Overall, the findings suggest that electric minibus taxis can be deployed in Stellenbosch’s paratransit system without fundamental operational restructuring, provided that charging infrastructure and charging management are aligned with service and grid constraints. • An agent-based model captures electric minibus taxi operations. • Driver choices include routing, stopping, idling, and charging. • The model is calibrated with empirical tracking data from Stellenbosch. • Home charging reduces depot queues and protects service quality. • Benefits above 22 kW per connector are limited in this case study.