🅿️ ParkIQ — Dynamic Parking Pricing System

🏆 IIT Guwahati Hackathon Project

🚗 A full-stack Flask web application for real-time dynamic parking pricing, featuring 3 intelligent pricing models, interactive charts, map view, and CSV data ingestion.

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📌 Overview

💡 ParkIQ solves the problem of static, inefficient parking pricing by dynamically adjusting prices based on real-time occupancy, demand signals, and competitor data. Built for the IIT Guwahati Hackathon, it brings smart pricing to parking lots.

🔗 GitHub Repository: tanishcode-12/ParkIQ

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✨ Features

• ⏱️ Real-time updates — simulates a new data point every 30 seconds
• 📂 CSV upload — drag & drop your own parking lot CSV
• 📊 3 live charts — Plotly.js with hover tooltips
• 🗺️ Map view — Leaflet + CartoDB dark tiles showing your lot vs competitors
• 📋 Comparison table — highlights lowest price per timestamp
• 🔐 User auth — Flask-Login with session management
• 🧠 3 pricing models — Baseline, Demand-Based, and Competitive

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🗂️ Project Structure

🅿️ parking_app/
├── 🐍 app.py                  # Flask routes & API
├── 📄 requirements.txt
├── 📁 models/
│   ├── 🐍 __init__.py
│   └── 🐍 pricing.py          # All 3 pricing models
└── 📁 templates/
    ├── 🌐 login.html
    └── 🌐 dashboard.html

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⚙️ Setup

🧰 Prerequisites

• 🐍 Python 3.8+
• 📦 pip

🪜 Steps

1. 📥 Clone the repository

git clone https://github.com/tanishcode-12/ParkIQ.git
cd ParkIQ/parking_app

2. 🧪 Create virtual environment

python -m venv venv
source venv/bin/activate        # Linux/Mac
venv\Scripts\activate           # Windows

3. 📦 Install dependencies

pip install -r requirements.txt

4. ▶️ Run the app

python app.py

5. 🌐 Open in your browser

http://localhost:5000

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🔐 Login Credentials

👤 Username	🔑 Password
admin	admin123
owner	owner123

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📄 CSV Format

📂 Your CSV file should contain the following columns:

📋 Column	📝 Description
LastUpdatedDate	📅 Date in DD-MM-YYYY format
LastUpdatedTime	🕐 Time in HH:MM:SS format
Occupancy	🚗 Number of occupied spots
Capacity	🏗️ Total capacity of the lot
QueueLength	🚦 Cars waiting (optional)
TrafficConditionNearby	🛣️ low / medium / high (optional)
VehicleType	🚌 car / bike / bus / truck (optional)
IsSpecialDay	🎉 0 or 1 (optional)
Latitude	🌍 Lat for geospatial pricing (optional)
Longitude	🌍 Lon for geospatial pricing (optional)

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🧠 Pricing Models

1️⃣ Baseline Linear

new_price = prev_price + α × (occupancy / capacity)

📈 Simple, interpretable model. Price grows proportionally with occupancy.

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2️⃣ Demand-Based

demand = 1.2×occ_rate + 0.8×queue − 0.5×traffic + 1.0×special_day + 0.6×vehicle_weight
final_price = base × (1 + λ × normalized_demand)   [clipped to 0.5×base … 2×base]

📊 Multi-factor model capturing richer demand signals including traffic, vehicle type, and special events.

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3️⃣ Competitive

🏆 Starts from baseline price, then adjusts based on competitor lots within 500m:

• 📉 If occupancy > 90% AND your price > avg competitor → reduce ₹5 (stay competitive)
• 📈 If competitor avg > your price → raise ₹3 (capture market)

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🚀 Extending the Project

🔧 Want to take ParkIQ further? Here are some ideas:

• 🗄️ Replace the in-memory USERS dict with SQLite/PostgreSQL + Flask-SQLAlchemy
• 📡 Add real competitor data via a parking API or manual input
• ☁️ Deploy to Heroku / Render with gunicorn app:app
• 🔌 Add WebSocket (Flask-SocketIO) for true real-time without polling

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🤝 Contributing

🙌 Contributions are welcome! Here's how you can help:

1. 🍴 Fork the repository
2. 🌿 Create a new branch (git checkout -b feature/your-feature)
3. 💾 Make your changes and commit (git commit -m 'Add your feature')
4. 📤 Push to the branch (git push origin feature/your-feature)
5. 🔁 Open a Pull Request

✅ Please make sure your code is clean and well-commented.

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👤 Author

Tanish — @tanishcode-12

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⭐ If you found ParkIQ helpful, consider giving it a star on GitHub!