Artificial Intelligence is redefining transportation globally, but can it solve mobility for Africa’s underserved cities?

At MacTech, we think it can. That’s why we’re prototyping ‘AI-powered’ ride matching, traffic heatmap predictions, and smart dispatching in our WakaForMe platform. The goal is simple: faster pickups, fewer driver idle times, and smarter city commuting.

The Problem
Africa’s urbanisation rate is rising faster than its transport systems. In cities like Osogbo or Ibadan, traffic patterns shift erratically due to informal street markets or ad hoc road closures. Traditional GPS-based algorithms used in the West often fail in this dynamic environment.

The AI Approach
We began training our system on three key data sets:
Historical ride data (over 1.2 million trip records)
Driver idle zones and availability windows
Local event calendars (e.g., market days, religious gatherings).

Using a lightweight AI model, we began predicting optimal dispatch zones for drivers, which improved match time by 34% in the pilot phase. Riders now wait an average of 4 minutes less.

What Else We’re Building

Surge prediction models to avoid peak-hour gridlock pricing
Driver scoring models based on ride completion, punctuality, and user feedback
Chatbot support using AI to guide users through booking issues in local languages.

The Ethics of AI in Africa
AI must be inclusive. We’re investing in bias-free training and working with universities to ensure that our data doesn’t reinforce social inequalities. For example, we anonymise location data to prevent over-targeting of wealthier areas.

African mobility doesn’t need to imitate Uber. It can leapfrog with context-aware, inclusive AI.

With the right partners, we aim to open-source parts of our model and collaborate with researchers across Africa. The future of transport won’t just be electric; it will be intelligent. And it will be built here.