CampusOR

Campus Online Queue & Reservation System

CampusOR is a smart, scalable virtual queue and reservation system for large campuses such as universities, hospitals, hostels, cafeterias, and administrative offices. It replaces physical queues with a real-time digital experience, reducing congestion and improving service efficiency.

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Table of Contents

• Overview
• Problem Statement
• Objectives
• Key Features
• System Architecture
• Technology Stack
• Folder Structure
• Novelty and Uniqueness
• Expected Outcomes
• Future Enhancements
• Conclusion

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Overview

Managing queues in large campuses often leads to long wait times, overcrowding, and inefficiencies. CampusOR introduces a virtual queue ecosystem where users can join queues digitally, track their position in real time, and receive timely notifications—without standing in physical lines.

The system combines real-time communication, predictive analytics, and role-based dashboards to deliver a seamless queue management experience.

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Problem Statement

Traditional campus queue systems face:

• Long physical queues and overcrowding
• No real-time queue visibility
• Manual token handling and errors
• Inefficient multi-counter coordination
• No waiting-time prediction
• Fragmented systems across campus

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Objectives

CampusOR aims to:

• Digitize queue and token management
• Reduce physical waiting and crowding
• Enable real-time tracking and alerts
• Support multi-counter operations
• Predict waiting times using data
• Provide a unified campus-wide solution

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Key Features

Users

• Join queues digitally via web or PWA
• Real-time queue position tracking
• Smart notifications (“You’re Next”)
• QR-based token verification
• Time-slot booking and rescheduling
• Offline PWA support with auto-sync

Operators

• Accept, skip, or recall tokens
• Pause and resume queues
• Multi-counter coordination
• Kiosk mode for public displays

Admin

• Queue and counter management
• Load balancing across counters
• Analytics: peak hours, wait time, efficiency
• Role-based access control

Machine Learning

• Predicts waiting time using:
  • Historical service data
  • Queue length
  • Counter availability
  • Time-based patterns

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System Architecture

• Clients communicate via REST APIs and WebSockets
• Backend manages queues, tokens, and users
• Real-time server syncs live updates
• ML service predicts wait times
• Databases store queue and analytics data
• Notification services push real-time alerts

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Technology Stack

• Frontend: Next.js
• Backend: Node.js, Express
• Database: MongoDB
• Real-Time: WebSockets, Socket.IO
• ML: Python (FastAPI)
• Notifications: WhatsApp, Telegram, Email (which ever is easier to integrate)
• Deployment: Docker, Vercel, AWS
• Security: JWT, RBAC

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Folder Structure

campusor/
├── frontend/        # Next.js 
├── backend/         # Express + MongoDB API
├── ml-service/      # FastAPI ML service
├── shared/          # shared types & schemas
├── infra/           # Docker & deployment
└── docs/            # architecture & API docs