1. Lisbon’s approach procedures to RWY 02
The ORCI project focuses on the North configuration at Lisbon Airport, with mixed mode operations (ARR/DEP mixed) conducted on RWY 02.
Arrivals to this runway are managed through RNAV1 instrument approach procedures known as Point Merge System (PMS), which is a systemised method for sequencing arrival flows developed by the EUROCONTROL Experimental Centre (EEC) in 2006. This procedure is designed to manage high traffic levels without the need for radar vectoring. It uses a predefined route structure made up of a merge point and several fixed legs located at the same distance from that point. Aircraft follow these legs until they are instructed to proceed directly to the merge point.
2. ๐๐ฎ๐๐ฎ ๐๐น๐ฒ๐ฎ๐ป๐ถ๐ป๐ด / Trajectory Validation
he ORCI project focuses on landing operations on runway RWY 02and does not consider departure operations. In addition, only flights that perform the PMS maneuver and whose trajectories are reasonably similar to the published procedure are included.
To prepare the data for training, it is necessary to carefully clean and select the flight trajectories. Although the PMS procedure is clearly defined, it is not always followed in practice. For example, during periods of low traffic, the sequencing legs may be skipped and aircraft may be guided directly to the initial fix (PESEX). For this reason, only those flights that closely follow the defined approach procedure have been selected, ensuring that the training data contains representative trajectories. The first image shows trajectories where the PMS is not used, while the second and third images show trajectories that follow the published PMS, approaching from the East and West, respectively.
3. Creating synthetic flights.
The main objective of ORCI is to predict the spacing between two consecutive aircraft at the point where the preceding arrives to PESEX (IF). For this purpose, each aircraft is paired with the one immediately ahead, designating the first to land as the preceding aircraft and the second as the follower. For subsequent pairs, the same aircraft may act as a follower in one pair and as the preceding aircraft in the next, depending on its position in the landing sequence.
Since the tool is developed using real traffic data, the range of situations it is exposed to is limited to those that occur in normal operations. However, for ORCI to provide reliable predictions, it must also be able to handle a wider variety of situations, including rare or extreme cases that are not present in historical data.
To overcome this limitation, synthetic training samples are created by shifting the timing of the leading aircraft in each valid flight pair forward or backward by a few seconds, while keeping the following aircraft unchanged and preserving the original 3D trajectories. This allows a single real flight pair to generate multiple scenarios with different spacing conditions.
This process makes it possible to cover a full range of situations, including:
- Real operational situations
- Excessive spacing situations
- Unrealistic situations where the following aircraft overtakes or collides with the leading aircraft
By expanding the original dataset from around 3,700 real flights to approximately 96,000 samples, the tool is exposed to separation values ranging roughly from 0 to 14 NM, enabling it to learn from both tighter and wider spacing scenarios than those observed in real operations.
4. Machine Learning Models: content coming soon.
This section is currently under development.
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