Optimal Flight Route Finder (Optimal Flight Route Finder)

Short name: flight-route-optimix — Project to compute and recommend optimal flight routes based on real-world airline data and user preferences.

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🔍 Overview

Optimal Flight Route Finder is a research/engineering project that models the global flight network as a graph and computes best routes between airports using classical graph algorithms (Dijkstra, A*), with options for multi-criteria optimization (cost, duration, number of layovers). The project demonstrates how MeTTa can be used for flexible graph representation and algorithm implementation alongside a Python-based data pipeline and optional web UI.

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✨ Key Features

• Shortest-path routing (Dijkstra, A*) between origin and destination airports.
• Multi-criteria route ranking: minimize cost, total duration, or layovers.
• Support for user preferences and constraints (preferred airlines, max layovers, departure/arrival windows).
• Import and process real-world datasets (OpenFlights or similar).
• Graph representation using MeTTa for symbolic rules and Python for numeric computation.
• Example CLI and starter web UI (Flask/React) for interactive queries.

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🛠️ Tech stack

• Core: Python 3.10+
• Graph & algorithms: networkx (or custom implementations), heapq for priority queues
• MeTTa: for symbolic graph representation and flexible rule-based reasoning
• Data storage: CSV / JSON for datasets; optional MongoDB or Firebase for persistence
• Optional UI: Flask backend + basic React frontend (or plain HTML templates)

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📁 Repository structure (suggested)

flight-route-optimix/
├─ data/
│  ├─ airports.csv         # airport metadata (IATA, ICAO, lat, lon, city, country)
│  ├─ airlines.csv         # airline metadata
│  └─ routes.csv           # route edges (source, destination, airline, duration, cost)
├─ metta/
│  └─ graph.meta           # MeTTa graphs & rules
├─ src/
│  ├─ data_loader.py       # dataset parsing and normalization
│  ├─ graph_builder.py     # build graph structures (NetworkX & MeTTa)
│  ├─ algorithms.py        # Dijkstra, A*, multi-criteria ranking
│  ├─ api.py               # Flask API for queries
│  └─ cli.py               # Command-line interface
├─ web/
│  ├─ frontend/            # React app or static pages
├─ notebooks/
│  └─ analysis.ipynb       # exploratory analysis and visualizations
├─ README.md
└─ requirements.txt

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⚙️ Installation

1. Clone the repo:

git clone https://github.com/<your-username>/flight-route-optimix.git
cd flight-route-optimix

2. Create a virtual environment and install dependencies:

python -m venv venv
source venv/bin/activate   # macOS / Linux
venv\Scripts\activate    # Windows
pip install -r requirements.txt

3. (Optional) Install MeTTa following its official instructions and place any .meta files under metta/.

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🚀 Quick start (CLI)

Example command to find the cheapest route from DEL to LHR:

python src/cli.py --origin DEL --destination LHR --optimize cost --max-layovers 2

Expected output: ordered route options with total cost, duration, layovers, and step-by-step flight legs.

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🔎 Algorithm notes

• Dijkstra: used for single-criterion shortest-path on a weighted graph (weight = chosen metric, e.g., duration or cost).
• A*: uses great-circle distance (haversine) as heuristic to speed up search for distance/duration optimization.
• Multi-criteria: implement Pareto-front filtering or weighted-sum scoring to rank candidate paths when optimizing for multiple objectives.
• Layover modeling: layover time is computed using scheduled arrival/departure times in the dataset; enforce minimum connection time (MCT) when building feasible paths.

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🧠 MeTTa integration (suggested)

• Store symbolic facts like (airport DEL), (route DEL LHR airline delta duration 350 cost 120).
• Write rule templates to expand edges or infer alternative connections (e.g., transits via hub airports).
• Use MeTTa to express high-level preference rules (e.g., prefer alliances, avoid red-eye flights) and let Python evaluate numeric scores.

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🧪 Data sources

• OpenFlights: airports, airlines, and routes datasets (CSV) — good for offline prototypes.
• AviationEdge / OpenSky / Official APIs: for more up-to-date schedules and live data (may require API keys).

Note: Check terms of service of any dataset/API used.

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✅ Usage examples

1. Minimal example (Python)

from src.data_loader import load_airports, load_routes
from src.graph_builder import build_graph
from src.algorithms import find_best_routes

airports = load_airports('data/airports.csv')
routes = load_routes('data/routes.csv')
G = build_graph(airports, routes)

results = find_best_routes(G, origin='DEL', destination='LHR', optimize='cost', max_layovers=2)
for r in results[:3]:
    print(r)

2. API request (Flask)

GET /route?origin=DEL&destination=LHR&optimize=duration&max_layovers=1

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🧩 Extensibility ideas

• Add live pricing and seat-availability by integrating airline/OTA APIs.
• Add user accounts, saved preferences, and history.
• Visualize routes on a world map using folium or D3.js.
• Integrate delay/real-time tracking from OpenSky for robust ETAs.

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🤝 Contribution

Contributions are welcome. Please open issues or pull requests. Follow these steps:

1. Fork the repo
2. Create a branch: feature/my-feature
3. Commit and push
4. Open a PR with description and tests/examples

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📄 License

This project is released under the MIT License. See LICENSE for details.

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📞 Contact

Project owner: Deepak Gupta

If you want the README tailored for a specific audience (hackathon submission, GitHub portfolio, or college project report), tell me and I'll adapt it quickly.