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AI-Optimized Smart City Traffic Management

Implemented an AI-optimized traffic management system that dynamically adjusts traffic light timing based on real-time traffic conditions, reducing congestion and improving traffic flow across the city.
Traditional fixed-timing traffic lights cause unnecessary delays and congestion. The city needed an intelligent system that could adapt to real-time traffic conditions, reduce wait times, and improve overall traffic flow during peak and off-peak hours.
We developed an AI-powered traffic management system that uses computer vision to analyze traffic flow in real-time and dynamically adjusts signal timing. The system coordinates multiple intersections to create 'green waves' and prioritizes emergency vehicles.
AI adjusts signal timing based on real-time traffic conditions
Automatic green light corridors for emergency vehicles
ML models predict traffic patterns and pre-optimize signals
City-wide traffic monitoring and control dashboard
Evaluated existing traffic infrastructure and identified optimization opportunities.
Developed machine learning models for traffic prediction and signal optimization.
Implemented system at 20 intersections for testing and refinement.
Expanded to 200+ intersections across the city with phased deployment.
Client
Metro City Council
Industry
Smart City & Transportation
Duration
8 months
Team Size
7 developers
Measurable impact and outstanding outcomes achieved through our solution.
Reduced Travel Time
Less Idle Time
Lower Emissions
Intersections Optimized
The impact on our city's traffic flow has been remarkable. Commuters are experiencing significantly shorter travel times, and we've seen measurable improvements in air quality due to reduced idling.
David Thompson
Director of Transportation, Metro City Council
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456 Orange Ave
Orlando, FL 32801
United States
Ground Floor, 1C Street 7A
Badar Com. Phase 5 Ext
DHA, Karachi, Pakistan
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