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Smart cities / Transportation

AI-assisted traffic management for a city council

A city centre's traffic signals were still running fixed timing plans set years earlier, with hundreds of junctions operating independently. We built a coordinated, AI-assisted traffic-management platform that adapts signal timing in real time to what is actually on the road.

ClientCity council (confidential)
IndustrySmart cities / Transportation
What we deliveredTraffic management platform, AI optimisation, sensor integration
AI-assisted traffic management for a city council
Real-timeSignals adapting to live demand
Corridor-wideHundreds of junctions coordinated
CompliantAir-quality zones within limits

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

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