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ATCS-GHReinforcement learning for traffic control
AI that gives Accra's fixed-timer traffic lights a brain — a reinforcement-learning controller that cuts simulated wait times by up to ~90% on peak-hour scenarios.
The problem
Lights that can't see the road
Ghana loses an estimated GH¢4.5 billion a year to traffic congestion. Most signalised junctions in Accra run on fixed timers that can't react to real traffic, and neighbouring lights don't coordinate — so clearing one junction just queues cars at the next.
Source: “The Cost of Gridlock,” Glima Research policy brief, February 2026.
The solution
A learning controller, no new hardware
A reinforcement learning agent (Double-DQN) watches each junction in real time — queue lengths, speeds, wait times — and chooses the signal phase every few seconds, continuously.
Across a corridor, independent agents learn to align their greens so cleared traffic keeps moving. It's a software layer over existing signal infrastructure — no new hardware required.
Results
Measured in simulation
- 85–92% reduction in average vehicle wait time versus a realistic fixed-timer baseline, on peak-hour scenarios
- Validated across 8 demand scenarios × 5 random seeds — it improves on every single one, not just on average
- Trains in about 2 hours on a laptop
- Fully open-sourced
All figures are compared against fixed-time control, which is what most Accra junctions actually run today. This is simulation-validated research, not a field deployment: no pilot, partnership, or city engagement exists yet. Outreach to transport authorities is a future goal.
Stack
How it's built
- Python
- PyTorch (Double-DQN)
- SUMO traffic microsimulation
- Godot 4 (real-time 3D visualiser)
- Flask
- WebSocket
Contact
Let's build something.
Investors, city and transport authorities, enterprise partners, or anyone curious about the work — the fastest route is a direct email.
ideally47@gmail.com