This project creates digital twins for air traffic, enabling real-time monitoring and advanced predictive analytics using machine learning and AI.
Status: Active development. Features and documentation may change frequently.
- Real-time flight tracking: Visualize and monitor flights as they happen.
- Air traffic congestion prediction: LSTM/GRU models to forecast congested airspace.
- Deviation detection: Autoencoders to identify abnormal flight patterns.
- Turbulence and climate event prediction: Combining CNN and LSTM models for weather-related forecasts.
- Air traffic simulation: “What-if” scenarios using reinforcement learning.
- Pollution prediction: Estimating environmental impact of air traffic.
- Delay prediction: Forecast flight delays using data-driven approaches.
- Docker
- Python >= 3.10
- Clone the repository
git clone https://github.com/v-mdev/airtraffic-digital-twin.git .
cd airtraffic-digital-twin- Configure environment variables
Fill the .env.template doc and move it:
cp .env.template src/airtraffic/config/.env
cd ..- Start services with Docker Compose
docker compose up -d- Verify that services are running
docker compose ps- Access the application
- Kafka UI:
http://localhost:9000 - InfluxDB:
http://localhost:8086 - Grafana:
http://localhost:3000
- Logs and debugging
docker-compose logs -f <service_name>
docker-compose down