CampusLine-Buster

A real-time cafeteria queue tracker built as a joint induction project between the Software Engineering and Data Science tracks. Students report wait times from their phones, and the live data stream feeds directly into a Data Science pipeline for analysis and prediction.

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Overview

Campus queues are unpredictable. CampusLine-Buster crowdsources wait-time reports from students already in the cafeteria, displays a live status to anyone checking before they leave their seat, and logs every report for downstream DS model training.

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Features

• Live wait-time display with crowd-sourced status updates
• Slider-based report submission — no typing required
• Colour-coded status indicator: calm, moderate, or hectic
• Live data stream log visible to all users
• Auto-refresh every 30 seconds
• Mobile-first responsive design
• In-memory API with a clean handoff format for the Data Science pipeline

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Tech Stack

Layer	Technology
Framework	Next.js 15 (App Router)
Runtime	React 19
Styling	Vanilla CSS (mobile-first)
API	Next.js Route Handlers
Fonts	Inter, JetBrains Mono (Google)
Dataset	campus_cafeteria_wait_times.csv

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Project Structure

src/
  app/
    globals.css       # Global design system and component styles
    layout.jsx        # Root layout with metadata
    page.jsx          # Main client-side UI
    api/
      wait-time/
        route.js      # GET and POST handlers for wait-time logs
dataset.ipynb         # Data Science exploration notebook
campus_cafeteria_wait_times.csv  # Historical training data

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Getting Started

Prerequisites

• Node.js 18 or later
• npm

Installation

git clone https://github.com/CommandLine-Protocol/CampusLine-buster.git
cd CampusLine-buster
npm install

Running Locally

npm run dev

Open http://localhost:3000 in your browser.

Building for Production

npm run build
npm run start

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API Reference

GET /api/wait-time

Returns the current list of wait-time reports in reverse chronological order.

Response

[
  {
    "id": 1,
    "timestamp": "12:01 PM",
    "location": "Main Cafeteria",
    "waitTime": 25
  }
]

POST /api/wait-time

Submits a new wait-time report.

Request Body

{
  "location": "Main Cafeteria",
  "waitTime": 15
}

Response

{
  "success": true,
  "data": {
    "id": 3,
    "timestamp": "12:10 PM",
    "location": "Main Cafeteria",
    "waitTime": 15
  }
}

Note: The current API uses an in-memory store that resets on server restart. A persistent database (e.g. SQLite, PostgreSQL, or Supabase) is the recommended next step.

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Data Science Integration

Every submitted report is structured to feed directly into a training pipeline. The campus_cafeteria_wait_times.csv file contains historical data used for initial model exploration in dataset.ipynb.

Planned DS work includes:

• Peak-hour prediction based on day of week and time
• Anomaly detection for unusually long waits
• A recommendation model for optimal meal times

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Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/your-feature
3. Commit your changes: git commit -m "Add your feature"
4. Push to the branch: git push origin feature/your-feature
5. Open a pull request

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License

This project is for educational purposes as part of an induction programme. No license has been formally assigned.