Using ADS-B Trajectories to Measure How Rapid Exit Taxiways Affect Airport Capacity

Manuel Waltert; Benoit Figuet;
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The capacity of an airport can be specified with a so-called capacity envelope, which indicates how many take-offs and landings an aerodrome is capable of handling per unit time. In this study, the capacity envelope of an airport is determined on the basis of Automatic Dependent Surveillance-Broadcast aircraft trajectories obtained via the OpenSky Network. Trajectories are classified as departures and arrivals by using rule-based algorithms. Subsequently, the time of landing or take-off is determined for all these flight movements. Since some of the trajectories used in this study are not entirely covered near the ground, an XGBoost model is used to improve the determination of the take-off and landing times. In a final step, the capacity envelope is determined. To this end, the number of take-offs and landings operated at an airport within 15-minute intervals are counted first. Then, the 92.5th percentile of departures is computed for all observed arrival counts. Finally, a concave, non-increasing piecewise-linear function is fitted to these quantile values. The method introduced in this study is subsequently applied to Lisbon Airport in order to evaluate if and how the construction of an additional rapid exit taxiway has affected its capacity. The results suggest that Lisbon Airport benefits from this rapid exit taxiway. Indeed, especially when the airport handles a high number of landings, the additional rapid exit taxiway appears to allow for a slightly higher number of departures.


The maximum throughput capacity, also known as the saturation capacity, of an airport’s runway system is defined as the number of arriving and departing aircraft movements it can perform over the period of one hour, while (i) the system experiences continuous demand, and (ii) air traffic control adheres to separation requirements [De Neufville et al. 2013]. When being operated at maximum throughput capacity, however, airports are subject to major delays and congestion, as no level of service (LoS) requirements, such as maximum acceptable waiting times for aircraft or manageable workloads for air traffic controllers, are considered. For this reason, airports rather rely on a concept called practical capacity to specify the number of take-offs and landings that can be realistically achieved per unit time [National Acedemies of Science, Engineering, and Medicine 2012]. In practice, a number of different definitions of practical capacity find application. As such, the concept of declared capacity, where an airport declares its capacity on the basis of empirical knowledge of experienced congestion [Airport Council International 2023], is widely used. Alternatively, the concept of practical hourly capacity described by the Federal Aviation Administration (FAA) is predominantly used in the United States of America [Federal Aviation Administration 1981]. Thereby, practical capacity is specified as the number of take-offs and landings that can be performed on a runway system under the condition that the average delay per aircraft movement is not more than four minutes.

Regardless of the definition of practical capacity applied at an airport, its magnitude depends on a multitude of factors. Most importantly, the number of runways available at an airport, their orientation and layout in relation to each other, dependencies between runways, as well as the runway configuration, which specifies how the available runways are utilised for take-offs and landings, affect capacity. This is particularly the case at airports where two or more runways are available. Moreover, for runways used for landings, the existence, location, and design of rapid exit taxiways (RET), which allow arriving aircraft to leave the runway at higher taxiing speeds, increase runway capacity as the average runway occupancy time (ROT) of landing aircraft is reduced. Besides that, an airport’s capacity is affected by the actual composition of demand. In this context, both the aircraft mix, which describes the types and size of aircraft using the runway system, as well as the movement mix, which describes the percentage of take-offs and landings, are of importance. Furthermore, separation standards describing the minimum horizontal and vertical distances to be maintained between two aircraft in flight by air traffic control at all times are relevant for the determination of practical capacity. Lastly, the condition of the air traffic management system and environmental factors, such as prevailing weather conditions affect the capacity of an airport system.

To describe the capacity of an airport, two different cases must be distinguished. If a relatively short period of time is considered, for which demand, aircraft and movement mix, the applied runway configuration, weather conditions, etc., are known or given, the capacity of an airport can be expressed with two integers: the maximum number of take-offs and landings that can be performed within a certain unit of time. To estimate the capacity of an airport for such a short-run case, analytical and simulation methods, as reviewed by Newell [1979; Odoni et al. 2015], can be applied. However, if airport capacity is considered over a long(er) period of time, it must be understood as a probabilistic quantity. In such a long-run case, airport capacity is often described in the literature with a concave, non-increasing envelope function \(\varphi\), which specifies the dependency of the number of departing aircraft from the number of arriving aircraft as follows

\[n_{DEP} = \varphi (n_{ARR}, \theta)\]

where \(n_{DEP}\) is the number of departing aircraft movements per unit time, i.e., the departure count, \(n_{ARR}= \{ 0,1,\ldots,n_{ARR}^{max} \}\) is the arrival count, \(n_{ARR}^{max}\) is the observed maximum arrival count, and \(\theta_{l} \in \theta\) refers to factors influencing the capacity envelope, such as weather conditions, runway configurations, etc. On the basis of observational data specifying the number of arrivals on and departures from an airport in \(t = 1,...,T\) time intervals of a given duration, the envelope function \(\varphi\) can be determined on an empirical basis1. Two different types of envelope functions are presented in the literature. Gilbo [1993] presents the capacity envelope, which is a piecewise-linear function enveloping the maximum values of all observed combinations of number of departures and arrivals per time interval. Simaiakis [2013] proposed the operational throughput envelope, which specifies the average number of departures and arrivals that can be handled by an aerodrome operated in a certain runway configuration per time interval.

To increase the robustness of an envelope function \(\varphi\) against outliers, which can either result from errors in data collection or from the airport operating outside of its practical capacity, extreme observations should be rejected. To this end, Gilbo [1993] applied a frequency-based filtering method, whereby occurrences of extreme observations which occur with a frequency below a given threshold value are excluded. Alternatively, Gilbo [1993:146] mentioned that rejection methods based on the "proximity of extreme observations to the nearest observations, [or] ranks of extreme values" could also be applied. Besides that, Ramanujam and Balakrishnan [2009] suggested the combined usage of quantile regression and linear optimisation to robustly estimate the envelope function \(\varphi\).

To determine the capacity envelope or the operational throughput envelope in practice, a rather substantial amount of data describing take-offs and landings executed at a certain airport is required. In this respect, Gilbo [1993] used a made-up data set of a fictitious airport to create a capacity envelope. Ramanujam and Balakrishnan [2009; Simaiakis 2013], however, employed data from the FAA Aviation System Performance Metrics (ASPM) database [Federal Aviation Administration] and, in the case of Simaiakis [2013], additional data from the Airport Surface Detection Equipment - Model X (ASDE-X)2 to create envelopes for airports of the New York Metroplex, which includes John F. Kennedy International Airport, Newark Liberty International Airport, and New York LaGuardia Airport.

Capacity and operational throughput envelopes as well as their dependence on runway configurations and weather conditions are already well-documented in the literature. To the best knowledge of the authors, however, there are no contributions describing how the capacity envelope of an airport is affected when the runway and taxiway system of an airport is modified. Furthermore, there is no contribution in the literature in which the capacity envelope of an airport is empirically determined exclusively on the basis of Automatic Dependent Surveillance-Broadcast (ADS-B) data. In light of these gaps, this study focuses on the questions of (i) how the capacity envelope of an airport can be determined on the basis of open-source ADS-B data obtained from the Opensky Network (OSN) [Schäfer et al. 2014], and (ii) how the capacity envelope of an airport is affected when one or more RET are added to the taxiway system of an airport. Consequently, this study contributes to knowledge by introducing a method to generate capacity envelopes on the basis of OSN data, by evaluating and discussing the impact of RET on capacity envelopes, and by presenting an example showing how the method described in this study can be employed in practice.

The remainder of this study is structured as follows: In Section 2, a method to determine the capacity envelope of an airport on the basis of OSN-sourced ADS-B data is presented. Subsequently, Section 3 contains a practical example in which the method presented in this study is applied to a real-world example concerning the airport of Lisbon, Portugal. Finally, the results and limitations of this study are discussed in Section 4, while conclusions and outlooks are provided in Section 5.


In the following, it is described how a capacity envelope function for an airport can be determined on the basis of OSN-sourced ADS-B data. The remainder of this section is divided into two parts: Section 2.1 describes the methods used to create the data set employed in this study, while Section 2.2 outlines the procedure applied to determine a capacity envelope function \(\varphi\) for an airport on the basis of OSN trajectory data.

Data collection and pre-processing

To generate a data set on the basis of which a capacity envelope function \(\varphi\) can then be estimated for an airport, four distinctive steps were carried out. First, an airport suitable for this study had to be selected. Then, OSN data for this airport had to be obtained and pre-processed. Finally, the data quality of the take-off and landing times estimated on the basis of the OSN data had to be improved by employing machine learning methods.

Selection of airport.

This study aims to measure how the addition of one or more RET to the taxiway system of an aerodrome affects its capacity envelope. Therefore, airports at which such an effect could be measured at all had to be identified first. For this purpose, it was determined which of the 100 largest airports in Europe3 built additional RET in the period between the years 2018 and 20214. Using the historical imagery feature provided by the Google Earth Pro software, the taxiway systems of the 100 largest European airports were systematically analysed for RET construction activities in the aforementioned time period. This analysis identified RET construction activities at seven airports, as summarised in Table 1.

Shortlist of European airports which added RET to their taxiway system between the year 2018 and 2021
ICAO Code Airport RET identifier Commissioning of RET(s) Runway
EPWA Warsaw Chopin Airport N2 September 2020 11
EVRA Riga International Airport Y July 2022 18
LEIB Ibiza Airport E4, E7 December 2020 06, 24
LIPZ Venice Tessera Airport F, G September 2020 04R
LPPR Porto International Airport F1 November 2021 35
LPPT Lisbon International Airport H1 December 2021 20
LSZH Zurich International Airport B7, L7 December 2018, July 2019 28

In a further step, the quality of OSN-sourced ADS-B data in the vicinity of the airports listed in Table 1 was examined. To this end, two data sets of historical ADS-B trajectories spanning one week were downloaded for each airport via the OSN using the traffic library [Olive 2019]: one data set for the period before RET construction and one data set for the period after RET commissioning. These two data sets were then inspected by hand for their feasibility for use in this study. In particular, it was checked whether aircraft taking-off and landing are visible, i.e., whether the coverage near the ground is given. It was found that the quality of the data varies greatly from aerodrome to aerodrome and year to year. While ground movements of most aircraft are visible for Zurich Airport, for example, the data quality in terms of ground coverage for other airports is significantly limited; trajectories of landing aircraft often end well before the threshold of the runway or only begin well after the end of the runway for departing aircraft. Furthermore, the choice of an airport suitable for this study must also take into account the influence of the COVID-19 pandemic on the traffic volume at the respective airports. To properly investigate the impact of additional RET on the capacity envelope, demand before and after the commissioning of the RET at an airport should be as unaffected as possible by COVID-related demand fluctuations on a monthly and annually aggregated level. Indeed, Zurich and Lisbon Airport are the only aerodromes listed in Table 1 that show both good5 ADS-B data quality and coverage as well as a negligible impact of COVID-19 on demand. After discussions with the local air navigation service provider Skyguide, however, Zurich Airport had to be excluded for further consideration in this study. According to information provided by Skyguide, the capacity of Zurich Airport operated in the runway configuration in which aircraft land on runway 28 is not limited by the maximum throughput of runway 28, but rather by airspace constraints. Indeed, the two RET B7 and L7 newly installed on runway 28 are used to ensure ’smooth’ day-to-day operations only. Because the maximum throughput of runway 28 in Zurich does not depend on the RET but on the airspace, Lisbon Airport is used in this study as a practical example to measure the influence of RET on the capacity of a runway system.

Data collection and pre-processing.

After Lisbon Airport, where RET H1 on runway 20 became operational in December 2021, was selected for further consideration, both a one-month pre-RET and a post-RET data set of ADSB-B trajectories were downloaded via OSN using the traffic library. To enable a comparison of the pre-RET and post-RET capacity envelopes in a later step, observation periods in which Lisbon Airport handled an equal amount of flight movements on the runway(s) of interest had to be determined. These periods were determined by downloading and systematically comparing OSN data over several months. In the end, October 2019 was selected for the pre-RET period and December 2022 for the post-RET period.

Airport chart of Lisbon Airport. RET H1 is indicated in red.

For both periods, data sets of OSN-sourced trajectories, which are later referred to as the pre-RET and the post-RET data set, respectively, are created as follows. In a first step, the trajectories of interest are retrieved from OSN using the traffic library [Olive 2019]. Lisbon Airport is equipped with only one runway, namely runway 02/20, and the newly constructed RET H1, see Figure 1 and Table 1, is used by aircraft landing on runway 20. Therefore, exclusively take-offs and landings on runway 20 are further considered in this study. To identify take-offs from and landings on runway 20, rule-based algorithms are applied. As such, all trajectories that both exhibit an average climbing rate of more than 500 feet per minute below 4,000 feet and spend at least 20 seconds in a box-shaped virtual zone located after runway 20, see Figure 2, are classified as take-offs. Similarly, all trajectories that show an average rate of climb less than 100 feet per minute and align with the extended runway axis for more than 30 seconds are assumed to be landings on runway 20. For illustrative purposes, a number of landings and departures identified in this process are depicted in Figure 2.