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CourseAerospace, UAM, Drones & Space Applications open

AI course in air traffic simulation

Online course of 300 hours in AI for air traffic simulation, structured in 6 modules covering air traffic management, trajectory data and models, machine learning, intelligent-agent simulation and validation, with an assigned technical tutor.

Duration3 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Learning outcomes

What you will be able to do

01

Understand air traffic management and its simulation.

02

Process ADS-B data and aircraft performance models.

03

Apply machine learning to air traffic predictions.

04

Simulate conflict resolution with intelligent agents.

05

Validate tools according to E-OCVM and safety criteria.

06

Analyse the regulatory requirements for AI in ATM.

07

Develop an AI application for simulated air traffic.

Profile

Who it's for

  • ATM systems engineers moving into AI-based traffic simulation.
  • Air traffic data scientists looking to build predictive and decision models.
  • Capacity analysts at air navigation service providers and airports.
  • Traffic simulation engineers adding machine learning to their toolkit.
  • Air navigation researchers.
  • U-space specialists working on drone and urban air mobility integration.
  • Engineering or science graduates aiming to join air navigation service providers.
Standards & tools

What you'll work with

  • Commission Implementing Regulation (EU) 2017/373 (requirements for ATM/ANS providers)
  • Commission Implementing Regulation (EU) 2021/664 (regulatory framework for U-space)
  • ICAO Doc 4444 PANS-ATM (procedures for air navigation services)
  • ICAO Doc 9854 (global ATM operational concept)
  • EASA Artificial Intelligence Concept Paper Issue 2 (guidance for machine learning applications in aviation)
  • EUROCONTROL E-OCVM 3.0 (operational concept validation methodology)
  • Regulation (EU) 2024/1689 (EU Artificial Intelligence Act)
  • BlueSky
  • Python with traffic
  • OpenSky Network
  • EUROCONTROL BADA
  • PyTorch
Syllabus

6 modules

  1. 1.1Airspace structure
  2. 1.2ATC services and sectors
  3. 1.3Capacity and traffic flow
  4. 1.4Separation and minima
  5. 1.5Flight trajectories and profiles
  6. 1.6Fast-time and real-time simulation
  7. 1.7Existing traffic simulators
  8. 1.8Traffic data and open sources
  9. 1.9SESAR and NextGen programmes
  10. 1.10Case study: congestion in an en-route sector
  1. 2.1ADS-B data and OpenSky
  2. 2.2Flight plans and formats
  3. 2.3BADA performance models
  4. 2.4Trajectory prediction
  5. 2.5Meteorology and en-route wind
  6. 2.6Data cleaning and fusion
  7. 2.7Trajectory representation
  8. 2.8Flight efficiency metrics
  9. 2.9Delay data and causes
  10. 2.10Lab: trajectory analysis with traffic
  1. 3.1Arrival time prediction
  2. 3.2Delay prediction
  3. 3.3Flight phase classification
  4. 3.4Trajectory anomaly detection
  5. 3.5Sector demand prediction
  6. 3.6Recurrent networks and transformers
  7. 3.7Graph neural networks
  8. 3.8Reinforcement learning for conflict resolution
  9. 3.9Model explainability
  10. 3.10Practical exercise: ETA prediction model
  1. 4.1BlueSky simulator
  2. 4.2Conflict detection and resolution
  3. 4.3Simulated controller agents
  4. 4.4Arrival sequencing
  5. 4.5Flow and slot management
  6. 4.6Integration of drones and U-space
  7. 4.7Urban air mobility
  8. 4.8Capacity scenarios
  9. 4.9Validation against real data
  10. 4.10Workshop: conflict resolution with RL in BlueSky
  1. 5.1E-OCVM methodology
  2. 5.2Safety assessment of ATM changes
  3. 5.3Controller human factors
  4. 5.4EASA requirements for AI
  5. 5.5Robustness and edge cases
  6. 5.6ATM system cybersecurity
  7. 5.7User acceptance
  8. 5.8Training data and bias
  9. 5.9Lessons from SESAR projects
  10. 5.10Case study: validation of a support tool
  1. 6.1Definition of the ATM problem
  2. 6.2Data collection
  3. 6.3Predictive or decision model
  4. 6.4Integration into a simulator
  5. 6.5Evaluation scenarios
  6. 6.6Safety and efficiency metrics
  7. 6.7Explainability analysis
  8. 6.8Technical documentation
  9. 6.9Report and presentation
  10. 6.10Final project: final review with the tutor
Methodology, software & labs

How you'll learn

The programme is 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so you can combine it with your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is BlueSky, Python with traffic, OpenSky Network, EUROCONTROL BADA and PyTorch, in free, academic or evaluation versions. Exercises are based on real procedures: predicting arrival times at an airport, conflict resolution with agents in BlueSky and efficiency analysis of real trajectories. Assessment is continuous by module plus the final project.

Standards covered: Commission Implementing Regulation (EU) 2017/373, Commission Implementing Regulation (EU) 2021/664, ICAO Doc 4444 PANS-ATM, ICAO Doc 9854, EASA Artificial Intelligence Concept Paper Issue 2, EUROCONTROL E-OCVM 3.0, Regulation (EU) 2024/1689.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Regulation (EU) 2017/373, Regulation (EU) 2021/664 and ICAO Doc 4444 PANS-ATM applied to real cases, not as a theoretical annex.
Industry software:BlueSky, Python with traffic, OpenSky Network, EUROCONTROL BADA, PyTorch, in free, academic or evaluation versions.
Final project:an AI application for simulated air traffic, from problem definition and data collection to simulator integration and evaluation, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Define the ATM problem
Frame an operational air traffic problem and the data sources needed to address it.
Collect and process data
Gather and clean ADS-B, flight plan and performance data for the case.
Build the model
Develop a predictive or decision model, such as ETA prediction or conflict resolution.
Integrate and evaluate
Integrate the model into a simulator and assess it with safety and efficiency metrics.
Final project
Document the explainability analysis and technical results, and present the report for your tutor's final review.
Career opportunities

Where this takes you

ATM systems engineerAir traffic data scientistCapacity analystTraffic simulation engineerAir navigation researcherU-space specialist
Admissions & scholarships

Requirements

  • No prior qualification is required.
  • An internet connection and a computer capable of running the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 300 hours over 3 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with BlueSky, Python with traffic, OpenSky Network, EUROCONTROL BADA, PyTorch, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute certificate, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

No prior qualification is required; you need an internet connection, a computer capable of running the programme software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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