PARK-N-GO
AI-powered smart parking management system with Computer Vision, Machine Learning, Dynamic Pricing, and Real-Time Monitoring to solve urban parking problems.
Visit websiteThe Problem
Urban areas face severe parking-related issues. Around 30% of urban traffic is caused by drivers searching for parking, with drivers spending 20–30 minutes finding a space. This leads to increased fuel consumption, carbon emissions, and inefficient parking space utilization.
The Solution
PARK-N-GO is an intelligent parking ecosystem that combines Computer Vision, Machine Learning, and Real-Time Analytics to detect vacant parking spots, predict parking demand, dynamically adjust prices, and enable seamless online reservations and payments.
Key Features
Real-Time Parking Detection: Computer Vision using OpenCV and YOLO detects occupied and vacant parking spots in real-time.
Dynamic Pricing: Prices adjust automatically based on demand, location, peak hours, and availability—increasing revenue and optimizing parking distribution.
Machine Learning Predictions: ML models predict parking demand and trends to help optimize city planning and parking management.
Mobile/Web Integration: Users can view available slots, reserve parking, navigate, and make online payments through integrated payment gateways (Google Pay, Paytm, PhonePe, Amazon Pay).
Technical Stack
Frontend: React.js / Next.js for user dashboard and parking slot visualization.
Backend: Python with OpenCV, TensorFlow, Scikit-learn, PyTorch, and YOLO for Computer Vision and ML inference.
Database: MongoDB for storing user information, parking slot status, bookings, and analytics.
Impact
PARK-N-GO reduces traffic congestion, minimizes time wasted searching for parking, lowers fuel consumption and carbon emissions, and optimizes parking space utilization. It creates economic benefits for parking operators and municipalities while improving urban quality of life.