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Tatiana Polishchuk, Linköping University, Sweden

Lucie Smetanová, Linköping University, Sweden

Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.

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New Insight Towards Characterization of the Terminal Areas

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Conference Name, Conference Dates

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Presentation Title

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TMAs experience performance degradation during busy hours
We work on comprehensive quantitative assessment of arrival operations within TMAs
Methodology developed by Eurocontrol Innovation Hub
Using set of metrics proposed by Eurocontrol + add new ones
Metrics previously used to evaluate performance of TMAs on three European airports
Goal is to find metrics to understand entry conditions to TMA

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Introduction

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Data

Historical database of Opensky Network

Accurate trajectory data for every second of flight in reach

Researched period:

Four weeks of October 2019 (peak traffic flows before pandemic)

Dataset:

Arrival trajectories inside TMAs (50NM around runway for Dublin)
High traffic hours 🡪 hours with high values of time in TMA

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Research Area

Three European airports – similar in size and yearly movements

Stockholm Arlanda airport 🡪 Open-loop vectoring
Dublin airport 🡪 Point Merge
Vienna airport 🡪 Trombones

Only the most frequented runway considered for each:

Stockholm Arlanda 🡪 01R
Dublin 🡪 28L
Vienna 🡪 16

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Airports

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Stockholm Arlanda

Dublin airport

Vienna airport

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Method

We study dependencies between two sets of metrics
Set A 🡪 candidate metrics for describing the entry conditions to TMA
Set B 🡪 Indicators used in previous studies

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Minimum Time to Final

Baseline metric for many of our metrics
Minimum time needed to get from any point within TMA to the runway
Rectangular grid with 1NM cell size
Aircraft trajectories normalized into grid dimensions
Best performer time to final within each cell
Visualized with heatmap

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Minimum Time to Final Visualization

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Stockholm Arlanda

Dublin airport

Vienna airport

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Set A metrics - Threshold

To understand the entry conditions, we need to know the position in time of the entry to TMA
Different for each hour as the traffic intensity varies
100 second iso-band of Minimum time to Final when most of the aircraft are present but still close to the border
Different iso-bands: 700, 800, 900, 1000, and 1100 seconds
Set for each hour by observing the positions of aircraft on their entry to TMA

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Threshold setting example

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Aircraft positions within different minimum time to final iso-bands

700s

800s

900s

16 aircraft

1 aircraft

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Other Set A metrics

Aircraft within the band:

Number of aircraft within the given iso-band corresponding to the threshold value for each one-hour period

Interarrival times:

Time between the consecutive arrivals to the TMA entry (threshold)
Aircraft ordered by the time they appear within the chosen iso-band

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Set B metrics: Time and Distance in TMA

Time in TMA:

Characterizes the temporal efficiency
Time aircraft actually spend in TMA
Starts at the entry to TMA (threshold) and ends when aircraft reaches zero altitude

Distance in TMA:

Characterizes the horizontal efficiency
Track-mile distance which aircraft fly within TMA

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Set B metrics: Sequencing Effort

Spacing Deviation:

Calculated for pair of consecutive aircraft (by arrival on runway)
Leader and trailer aircraft
Difference between their respective minimum times to final at time t of aircraft pair

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Sequencing Effort:

SE at time t is the difference between the Spacing Deviation at that time and the one close to landing (30s in this case)

To calculate the sequencing effort, we need to present its base calculation, the spacing deviation. Spacing Deviation is calculated for pair of consecutive aircraft sorted by their time of arrival to the runway, tagged�as the leader and the trailer according to which aircraft landed first. The Spacing Deviation is defined as the difference�between the respective minimum times to final at time t of aircraft pair. The spacing deviation�reflects information about the control error, which is the accuracy of spacing around the airport. Our new metric Sequencing Effort, is calculated as a difference between the spacing deviation at�the given time horizon and the one close to the final which is 30 𝑠 in�this work. Sequencing effort characterize the efficiency of the sequencing methods used to safely manage the aircraft flows.

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Set B: Vertical Deviation and Additional Fuel Burn

Vertical Deviation:

Characterizes the vertical efficiency
Difference between the actual vertical profile flown and the reference vertical profile
Continuous Descent Operations (CDO) as the reference

Additional Fuel Burn:

Characterizes the environmental impact
Difference between the fuel consumption of real trajectory and the one for reference profile

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Results: Stockholm Arlanda

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The goal is to identify indicators which strongly correlate with most of the performance metrics
Strong or moderate correlations for Threshold

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Results: Dublin

Median values of Interarrival Times metric demonstrate promising correlations at Arlanda and Dublin airports
Potential candidate metric for characterization of the arrival sequence at the TMA border

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Results: Vienna

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Strong correlation of Threshold Metric and Sequencing Effort metric uncovers the way how the arrival sequence is organized, and the control effort required to arrange that

Another interesting observation is strong correlation between the Threshold and the new metric ,Sequencing Effort�for most of the airports.�Despite the fact that these two metrics are calculated based on the same Minimum Time to Final, they are quite different�and represent the phenomena of completely different nature. While Threshold reflects the overall traffic intensity,�Sequencing effort is representing the mutual dependencies between the aircraft pairs. Threshold is calculated for�the whole hour while the sequencing effort is defined for the consequent aircraft pairs. Strong correlations between these two uncover�the interplay between the way how the arrival sequence is organized and the control effort required to arrange this�sequence in suitable order at the final approach.

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Conclusions

Contribution for performance evaluation framework targeting a comprehensive quantitative assessment of the terminal areas
We newly introduced the Threshold and Sequencing Effort metrics
Significant dependencies between metrics were shown
Further studies include validation usability of the candidate indicators

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