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2024Urban Flow

Smart City Traffic Optimization

PythonGraph Neural NetworksIoTKubernetesGCP

SYSTEM VISUALIZATION

-30%

Commute Time

-15%

CO2 Emissions

+25%

Throughput

-4min

Emergency Response

The Challenge

Urban congestion was costing the city millions in lost productivity and increasing CO2 emissions. Static traffic light timers were inefficient and unable to adapt to accidents, weather, or special events.

The Solution

We deployed a city-wide graph neural network that models traffic flow as a fluid dynamic system. The AI controls traffic lights in real-time, predicting congestion before it happens and rerouting flow dynamically.

Key Deliverables

  • Architecture Blueprint
  • Production Model Weights
  • API Documentation
  • Dashboard Interface

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