City-Wide Traffic Digital Twin
Traffic lights ran on fixed timers, ignoring actual flow. One accident would gridlock the city for hours.
A rapidly growing metropolis faced chronic gridlock. Building new roads was too slow; they needed to sweat existing assets better.
•10k+ sensors
•Complex topology
•Multi-modal transport
•Public pressure
Data Ingestion
Streaming pipeline for cameras, induction loops, and GPS probes.
Simulation Engine
Micro-simulation of every vehicle to test signal timing strategies.
Adaptive Control
AI adjusting traffic lights reacting to live accidents or rain.
1
Data Quality
Cleaning noisy sensor data in real-time to prevent erratic signal behavior.
2
Compute Scale
Simulating a whole city required massive parallel processing.
22%
reduction in average commute times
15%
decrease in idle vehicle emissions
30%
faster emergency vehicle response
Integration
of 10,000+ IoT sensors
We created a live Digital Twin of the city's transport network. It uses AI to watch traffic flow and change traffic lights dynamically. It smooths out waves of traffic and opens 'green corridors' for emergency responders.
PythonApache FlinkSumoKepler.glAWS
