01 The challenge
Congestion was costing the local economy heavily, bus punctuality was poor, and two zones were breaching legal air-quality limits — putting the council under regulatory pressure to act within a fixed deadline. Junctions had no real-time coordination and the control room could only react after queues had formed.
02 What we did
- Junction and sensor integration: signal controllers, loop detectors, ANPR and bus-location feeds brought into one platform
- Adaptive signal optimisation — machine-learning models that adjust green time and offsets across corridors as demand changes through the day
- Bus priority at key junctions using live vehicle positions
- Air-quality zone strategies that smooth flow and cut idling where limits were breached
- Control-room dashboards with incident detection, manual override and a full audit trail of every timing decision
03 The results
- Peak-hour congestion reduced measurably across the city centre
- Bus punctuality improved on priority corridors
- Air-quality zones brought back within legal limits
- Faster emergency-vehicle response through coordinated signals