🚦 SMART FLOW: AI-Powered Traffic Management System

Revolutionizing urban traffic management through AI-driven intelligent traffic light control systems that adapt to real-time congestion patterns, reducing wait times and emissions while enhancing city mobility.

📑 Table of Contents

• The Urban Challenge
• Our Solution
• Core Technologies
• System Architecture
• Key Results
• Real-world Impact
• Future Roadmap
• Team Members

🌆 The Urban Challenge

In India's rapidly growing cities, traffic intersections have become critical bottlenecks, where:

• Average commuters lose 2-3 hours daily in traffic congestion
• Conventional timer-based systems fail to adapt to dynamic traffic patterns
• Manual traffic management is inconsistent and resource-intensive
• Vehicle emissions from idling contribute significantly to urban air pollution

Smart Flow transforms these painpoints into opportunities for creating smarter, more efficient urban mobility.

💡 Our Solution

Smart Flow's Adaptive Intelligence System

Vehicle Count Detection

vehicle_count.mp4

Reinforcement Learning

before_dqn.mp4

after_dqn.mp4

Unlike traditional systems, Smart Flow:

• Sees traffic conditions in real-time through computer vision
• Learns from historical patterns using reinforcement learning
• Adapts signal timings dynamically to optimize flow
• Coordinates across multiple intersections for network-wide efficiency

⚙️ Core Technologies

Category	Technologies
Computer Vision
AI Models
Simulation
Implementation
Data Analysis
Visualization

🏗 System Architecture

1. Data Acquisition Layer

   • Traffic cameras capture video feeds
   • Image processing converts to 20 frames/min
   • Grayscale conversion optimizes computational efficiency

2. Analysis Engine

   • Canny Edge Detection identifies vehicle outlines
   • Semantic segmentation differentiates vehicles from surroundings
   • Hough Line Transform separates lanes for accurate counting

3. Decision Intelligence

   • Reinforcement learning model evaluates traffic density
   • Reward system optimizes for minimum wait time
   • Historical pattern analysis informs predictive adjustments

4. Control Interface

   • API connects to existing traffic light controllers
   • Dashboard provides real-time system monitoring
   • Performance analytics track system improvements

📊 Key Results

Efficiency Improvements

Wait Time Reduction

🌍 Real-world Impact

Environmental Benefits

• Reduced Emissions: 18% reduction in CO2 emissions from decreased idling time
• Fuel Savings: Estimated 15-20% reduction in fuel consumption at optimized intersections

Economic Value

• Commuter Time Savings: Average 12 minutes saved per commuter per day
• Implementation Cost: 5-10x lower than infrastructure expansion alternatives
• ROI Timeline: Initial investment recovered within 18-24 months through reduced congestion costs

Urban Quality of Life

• Reduced Stress: Decreased unpredictability in commute times
• Emergency Response: Priority routing for emergency vehicles reduces response times by 23%
• Public Transportation: Improved schedule reliability for buses operating on optimized routes

🚀 Future Roadmap

• V2X Integration: Connect with vehicle-to-infrastructure communication systems
• Pedestrian Intelligence: Incorporate pedestrian density in optimization algorithms
• Weather Adaptation: Dynamically adjust for adverse weather conditions
• Citywide Deployment: Scale from individual intersections to citywide traffic optimization
• Public API: Provide traffic prediction data for navigation apps and urban planners

👥 Team Members

Name	GitHub
Om Mukherjee	GitHub
Abhishek Kotwani	GitHub
Aryan Yadav	GitHub

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