City-Wide Traffic Digital Twin

Client
Government
Industry
Real Estate
Services
Data & Analytics
Case Study Cover

The Challenge

Traffic lights ran on fixed timers, ignoring actual flow. One accident would gridlock the city for hours.

Client Overview

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

Solution Components

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.

Challenges & Risks

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.

Key Impact

22%
reduction in average commute times
15%
decrease in idle vehicle emissions
30%
faster emergency vehicle response
Integration
of 10,000+ IoT sensors

The Solution

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.

Tech Stack

PythonApache FlinkSumoKepler.glAWS
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