In this context, the future of air traffic management is expected to rely more heavily on data-driven and predictive tools to support operational decision-making.

Traditional arrival management systems provide support for sequencing aircraft and estimating arrival times. However, many operational challenges remain, particularly when it comes to anticipating how the distance between aircraft will evolve during the arrival phase. Variability in aircraft performance, weather conditions, and operational constraints can make spacing difficult to predict accurately.

This is where projects like ORCI contribute to the evolution of ATM.

By focusing on aircraft spacing prediction, ORCI explores how machine learning models can provide early and more accurate estimations of future distances between aircraft. This type of information could complement existing tools by giving controllers additional insight into how the situation will develop.

Looking ahead, predictive capabilities could support:

• More stable arrival sequences, reducing the need for tactical interventions
• Improved workload management for controllers
• Better use of advanced procedures, such as RNAV-based operations
• Enhanced robustness in the presence of uncertainty

In particular, environments such as Barcelona, where trombone manoeuvres are used, or Lisbon, with Point Merge operations, highlight the importance of anticipating how spacing evolves over time. In these contexts, early predictions could help optimise sequencing decisions and improve overall efficiency.

While these concepts are still under development and evaluation, the results obtained within ORCI suggest that predictive tools have the potential to play a key role in future ATM systems.

The first presentation took place at the 15th EASN International Conference, held in October 2025 in Madrid. At this event, the ORCI team presented a poster describing the project’s methodology and the development of machine learning models designed to predict the future spacing between arriving aircraft. The poster introduced the overall research framework, including the use of operational data and the modelling approach used to estimate how spacing between aircraft may evolve during the arrival phase.

Later in the year, ORCI was also presented at the SESAR Innovation Days 2025, one of the main European events dedicated to research and innovation in air traffic management. The poster presented at this event focused on the Human-in-the-Loop (HITL) validation activities planned within the project. These validations aim to assess the operational usefulness of the ORCI predictions by involving air traffic controllers in simulated scenarios where they can interact with the system and evaluate how the predictions support spacing management.

Both posters provide an overview of the ORCI project, including its objectives, the operational motivation behind aircraft spacing prediction, and the case studies used in the research.

The materials presented at these events are available in the Resources section of the ORCI website, where visitors can access the posters and learn more about the project’s research activities.

Participation in these events reflects ORCI’s commitment to engaging with the wider ATM research community and sharing the progress of the project. The team also expects to continue presenting ORCI at several conferences and research events throughout 2026, further disseminating the results and insights developed during the project.

The INECO and ISA Software team at the 15th EASN Conference in Madrid, Spain

The INECO and ISA Software team at the SIDs 2025 in Lake Bled, Slovenia

Accurate aircraft spacing is essential for safe and efficient operations in busy airspace. Air traffic controllers must continuously manage the sequence of arriving aircraft while ensuring that safe separation is maintained. Predicting how spacing between aircraft will evolve during the arrival phase is therefore a key challenge, especially in complex and dynamic operational environments.

The ORCI project focuses on developing predictive models capable of estimating the future distance between aircraft earlier in the arrival process. By anticipating how spacing will evolve, these predictions can help support improved sequencing decisions and contribute to more stable arrival flows.

To evaluate these approaches in realistic operational environments, ORCI focuses on two specific case studies. The first one is Barcelona, where arrival operations often involve the use of trombone manoeuvres to manage aircraft sequencing. The second case study is Lisbon, where the Point Merge System is used to organise arrival flows. Both procedures are based on RNAV operations, providing suitable operational contexts to analyse spacing evolution and assess the performance of predictive models.

Through collaboration between research and operational partners, the project aims to evaluate these models and explore their potential contribution to future air traffic management tools.

In this news section, we will share updates about the progress of the ORCI project, including technical developments, participation in events, and insights gained throughout the project.

Stay tuned for future updates as ORCI continues to advance research on aircraft spacing prediction.