🚀 Building an AI-Powered Adaptive Queue System for Large-Scale Events

#ai #automation #showdev #webdev

Large-scale sporting venues often struggle with one persistent problem: long queues and inefficient crowd distribution.
This project was developed using AntiGravity, leveraging AI-assisted engineering to rapidly design, iterate, and deploy a full-stack solution.

From food stalls to entry gates, attendees spend unnecessary time waiting — not because capacity is insufficient, but because demand is unevenly distributed.

To tackle this, I built an AI-powered Adaptive Queue System that replaces physical queues with a dynamic, intelligent system.

🧠 The Idea

Instead of standing in line, users:

Join a virtual queue
Get assigned to the optimal service point
Receive updates on wait time and movement

The system continuously optimizes crowd flow in real time.

Think of it as a queue optimization engine, not just a ticketing system.

⚙️ Tech Stack
MERN Stack
MongoDB → data persistence
Express.js → backend APIs
React → frontend UI
Node.js → server runtime
Firebase
Real-time capabilities
Push notifications
Google Cloud Run
Backend deployment
Scalable containerized environment
🏗️ System Architecture
Backend (Node + Express)
REST APIs for:
Event management
Queue operations
Authentication
Core logic:
Queue position assignment
Wait time estimation
Load balancing across stalls
Frontend (React)
User interface for:
Viewing events
Joining queues
Tracking queue status
Cloud Infrastructure
Backend deployed on Google Cloud Run
MongoDB hosted remotely (Atlas)
Firebase for real-time extensions
🔄 How It Works

Event Creation

Admins create events with:

Venue
Date

Expected capacity

Joining the Queue

Users:

Select an event
Join a virtual queue

System:

Assigns queue position
Stores data in MongoDB

Intelligent Queue Handling

The system:

Tracks queue length per stall
Estimates wait time
Can dynamically reroute users

☁️ Deployment (Google Cloud Run)

One of the most interesting parts was deploying the backend using Cloud Run.

Key challenges solved:
Handling environment variables (MongoDB URI, JWT)
Ensuring server listens on process.env.PORT
Fixing build issues (package-lock.json sync)

📊 What Makes This “AI-Powered”?

Instead of static queues, the system uses:

Dynamic scoring logic
Real-time queue balancing
Intelligent stall assignment

Example concept:

Score = QueueLength × AvgServiceTime

Lower score → better stall

🚀 Results
Eliminates physical queues
Reduces waiting time
Improves crowd distribution
Scales easily using cloud infrastructure
🔮 Future Enhancements
Real-time Firebase sync
Indoor navigation
Predictive wait time using historical

data
Admin analytics dashboard
🎯 Final Thoughts