
AI Traffic Congestion Notification
Project Description:
The AI Traffic Congestion Notification System is a smart solution that uses artificial intelligence and IoT sensors (or simulated data) to monitor traffic flow in real-time and notify users about traffic congestion on specific routes. The system aims to provide commuters with early warnings and suggest alternative routes, helping reduce travel time and manage urban traffic more efficiently.
This project can be integrated with Google Maps API or simulated using mock data and computer vision models trained on video feeds.
Core Objectives:
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Monitor traffic density in real-time using AI (image, sensor, or simulated input)
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Detect and predict traffic congestion
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Notify users via web/app interface or SMS/email
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Suggest alternate, less-congested routes
Key Features:
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Traffic Monitoring Module
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Data sources:
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Simulated traffic density data
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Live feed from IoT traffic sensors/cameras (if available)
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Public APIs (e.g., Google Maps, TomTom)
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Congestion Detection (AI Module)
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Use AI to:
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Detect vehicles from images/videos using YOLO or OpenCV
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Predict congestion levels using historical patterns + real-time data
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User Notification System
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Real-time alerts to users:
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Web notifications
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SMS/email alerts (via Twilio or Mailgun)
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Includes details like location, severity, and suggested detours
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Web Dashboard
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Map-based view showing traffic conditions
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Red/yellow/green indicators for congestion levels
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Live update of alerts
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Route Optimization (Optional)
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Suggest alternate routes using shortest-path algorithms (like Dijkstra)
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Show travel time estimates for multiple options
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Tech Stack:
Frontend:
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HTML, CSS, Bootstrap
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JavaScript + Google Maps API
Backend:
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Node.js / Java / PHP (server logic)
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Python (for AI congestion analysis)
AI/ML Stack:
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OpenCV
,TensorFlow/Keras
,YOLOv5
orYOLOv8
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Pretrained models to detect vehicles and track movement patterns
Database:
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MySQL / MongoDB – for storing traffic reports, alerts, user history