Fornes, H. & Balakrishnan, H.
Title: Analysis of Potential Implementations of Pushback Rate Control at LaGuardia Airport.
Number of words: 3536
Number of tables: 1 (250 words)
Number of figures: 3 (750 words)
Author 1:
Name: Hector Fornes Martinez
Affiliation: Massachusetts Institute of Technology
Mailing address: Massachusetts Institute of Technology. Office 35-217
77 Massachusetts Avenue.
02139 Cambridge, MA
USA
Telephone number: (+1) 617-308-9877
Email address: hfornes@mit.edu
Author 2:
Name: Hamsa Balakrishnan
Affiliation: Massachusetts Institute of Technology
Mailing address: Massachusetts Institute of Technology. Office 33-328
77 Massachusetts Avenue.
02139 Cambridge, MA
USA
Telephone number: (+1) 617-253-6101
Email address: hamsa@mit.edu
1
Fornes, H. & Balakrishnan, H.
2
ABSTRACT
Implementations of surface traffic management strategies at congested airports have the potential
to yield significant benefits, but must account for the constraints and objectives of multiple stakeholders.
This paper considers the implementation of a pushback rate control strategy at LaGuardia airport in New
York. This class of control policies regulate departure push-back rates by holding aircraft at their gates
during congested periods, in a manner than maintains the departure throughput of the airport.
The pushback rate control strategy accounts for gate conflicts that occur between departures held
at the gate and arrivals assigned to those gates, by releasing the departures when the corresponding arrivals
land. Another practical concern that arises at congested airports is that of long gate-holds, which can be
inconvenient to air carriers. To this end, we analyze the distribution of gate-hold times that would be
expected at LaGuardia airport, and explore potential implementations of a pushback rate control policy in
which gate-holds would be limited to a maximum of 10 minutes and 15 minutes. The results show that
although the total benefits decrease with these constraints, significant reductions in taxi-out times are still
possible, and the resultant policies maintain both efficiency (that is, gate-hold times translate to taxi-out
time reductions) and fairness (that is, the share of taxi-out savings received by an airline is approximately
the share of departures operated by the airline).
Fornes, H. & Balakrishnan, H.
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MOTIVATION
Surface congestion at major airports results in excessive taxi-out times, and increased fuel burn and
emissions. For example, LaGuardia (LGA) Airport in New York is congested 10-15% of the time, that is,
the number of active flights on the surface is higher than needed to sustain departure throughput. The result
is taxi-out times of more than 50 minutes, even though the unimpeded (free-flow) taxi-out time is around
15 minutes.
Surface congestion management policies have the potential to reduce the taxi-out times, and
therefore reduce emissions of pollutants such as Carbon Dioxide, Hydrocarbons, Nitrogen Oxides and
Particulate Matter, at congested airports. The implementation of such policies would bring benefits for the
different stakeholders, but practical constraints such as gate use conflicts and the length of taxi-out times
must be considered.
The pushback rate control strategy recommends push-back rates for departures from gates during
congested periods, in order to prevent excessive congestion on the surface, while still maintaining the
departure throughput of the airport. It accounts for gate conflicts that occur between departures held at the
gate and arrivals assigned to those gates, by releasing the departures when the corresponding arrivals land.
A practical concern for air carriers is that of long gate-holds, which can be inconvenient and also affect the
on-time departure performance. To this end, we analyze the distribution of gate-hold times that would be
expected at LaGuardia airport, and explore potential implementations of a pushback rate control policy in
which gate-holds would be limited to a maximum of 10 minutes and 15 minutes. We evaluate the impact
on the taxi-out times and gate-holds of different airlines, under the different variants of pushback rate
control: Unrestricted, 10-minute, and 15-minute maximum limits on the gate-hold times.
PUSHBACK RATE CONTROL STRATEGIES
Background
The policies presented below are variants of the N-control framework based on the virtual queuing
of departures (Feron et al. 1997), (Burgain, Feron, and Clarke 2008). This framework makes it possible to
define policies that reduce fuel burn and emissions at airports (Simaiakis and Balakrishnan 2009),
(Simaiakis and Balakrishnan 2010).
At the beginning of each time-period, the departing surface traffic is evaluated, and further
pushbacks as regulated if needed. In this manner, the control policy prevents the presence of idling aircraft
that do not lead to additional departure throughput; the number of taxiing aircraft and the average taxi-out
times are thereby reduced.
Figure 1 depicts the relationship between the number of departures active on the surface (referred
to as N) and the takeoff throughput. The relation indicates that lower values of active departing aircraft on
the surface lead to increments of departure throughput; however, this is only valid up to a saturation
threshold after which the system achieves the departure runway capacity, and no additional flights can
takeoff, regardless of how many are ready to do so. Such a saturation threshold is referred as N*, and it
defines a border between the number of aircraft that will contribute to takeoff throughput and those aircraft
that will not, and therefore will idle burning fuel and emitting gases. This traffic classification allows the
possibility to take action when the number of departing aircraft on the surface (N) at a particular moment
exceeds a threshold value related with N*. This threshold value is Nctrl, which is always larger or equal
than N* (Nctrl≥ N*) and when N>Nctrl, the system requires those aircraft requesting pushback, in excess
of Nctrl, to wait at the gate until the surface traffic diminishes and therefore N becomes smaller or equal
than Nctrl.
Fornes, H. & Balakrishnan, H.
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Figure 1: Departure throughput as a function of the number of aircraft taxiing out, for the (VMC; 31|4)
configuration at LGA. Source: (Simaiakis 2013)
In the literature cited above there are two policies to go about limiting pushbacks based on Nctrl;
the first requires controllers to release as many aircraft as required to reach the threshold whereas the second
involves recommending a pushback rate to controllers. There are several implementation challenges
associated with the former because it involves more active control intervention on the controller side
compared to the latter (Sandberg et al. 2014).
Definition of pushback rates
The recommended pushback rates are determined based on the conservation principle applied to
the taxiway system. At the beginning of each time period, the model estimates the number of aircraft active
in the system until that moment (current surface traffic, curN), the number of expected aircraft that will
leave the system during that time period (expected takeoff throughput), and the desired number of aircraft
on the ground. The current traffic is an observed variable, the takeoff throughput is determined following
the guidelines below (regression trees), and the desired level of traffic is given will by the Nctrl values.
The conservation principle provides a recommended pushback rate as indicated in the following
equation:
Push rate= Nctrl + throughput – curN
This equation is evaluated at the beginning of every time period, and the pushback rate is communicated to
the air traffic controllers.
Estimation of parameters
In order to determine the recommended pushback rates, one needs to predict the takeoff throughput
and the unimpeded taxi-out times, and decide on the length of the time-period. The takeoff throughput is
determined from regression trees (Simaiakis 2013) as a function of the runway configuration, the number
of arrival rates, visibility conditions (IMC or VMC) and the weather, using the Route Availability Planning
Tool (RAPT) as the weather indicator (DeLaura et al. 2008). When building the model, the runway
configuration, the arrival rate, and the visibility conditions are obtained from the Federal Aviation
Administration (FAA) Aviation System Performance Metrics (ASPM) database; the RAPT weather
Fornes, H. & Balakrishnan, H.
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archives were provided by Lincoln Labs. During implementation, all these parameters can be either found
in the monitors of the control tower (runway configuration, visibility conditions, and RAPT) or estimated
based on the tower equipment (arrival rate). The availability of these variables in the tower allow for the
use of regression trees during implementation.
The unimpeded taxi-out time is defined by the FAA as the taxi-out time under optimal operating
conditions, when neither congestion, weather nor other factors delay the aircraft during its movement from
gate to takeoff. (Simaiakis 2013) did a further detailed analysis of all the factors that need to be included in
the unimpeded taxi-out time calculations. This time was obtained from a convex curve-fit correlating the
taxi-out time as a function of the effective surface traffic for each departing flight, defined as the sum of the
aircraft taxing out at the time of the pushback of that specific flight plus the number of aircraft that push
back while the specific flight is travelling to the departure runway (Simaiakis 2013). Four departure
terminals and ten important runway configurations at LGA were modeled using ASPM data. The
unimpeded taxi-out times obtained ranged from 11 minutes to 17 minutes. Based on an analysis of the
typical unimpeded times, the length of the time-interval over which the pushback rate is determined was
set equal to 15 minutes.
Model structure
The overall structure of the model is as follows: The model first analyzes the baseline case with
empirical data, and then simulates the performance of the different metering policies proposed. The model
re-simulates each metering policy resolving those additional gate conflicts caused by the metering policy.
One of the main consequences of metering is an increase in the time spent at the gate. There is a chance
that the next arriving aircraft to use that gate touches down while a departure is being held at the gate, which
causes a gate conflict. In order to resolve such gate conflicts, the departure involved in a gate conflict are
cleared for pushback immediately after the arriving aircraft lands. In this case, the model does not consider
Nctrl, because the maximum average frequency of additional conflict occurrence is 0.4 conflicts per hour,
which is small compared to Nctrl values.
The analysis and simulations carried out rely on three datasets. First, ASPM provides data on actual
arrival and departure (call for pushback) times, “wheels-on” time (arriving aircraft touch down), “wheelsoff” time (departing aircraft rotate-and-lifts), air carrier, year, month, day, runway configuration and
visibility conditions. These data is complemented with terminal and gate data from Flightstats.com; these
data is important to carry out the gate conflict simulation. Finally, as indicated previously, the model also
uses RAPT data to have a better accuracy in accounting for weather. These models have been previously
tested in field-trials at Boston Logan Airport, as presented in (Sandberg et al. 2014) and (Simaiakis et al.
2011).
IMPACT OF DEPARTURE METERING ON GATE HOLDS
The developed model makes it possible to evaluate the length of gate holds. Figure 2 depicts the
percentage distribution of holding times for each airline. It indicates the magnitude and the distribution of
the impacts of the metering policy on gate operations. This is one of the key indicators for airlines because
this represents the most noticeable change in the airline surface operations.
Fornes, H. & Balakrishnan, H.
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Figure 2: Duration of gate holds by airline at LGA airport during the period July 1 2013 to August
31 2013
Figure 2 shows that for all the airlines, more than 75% of the flights do not receive any gate-holds.
For the 1 to 10-minute range, between 1% and 5% of flights are held for each airline. Less than 1% of
flights are held for more than ten minutes. These results suggest that the percentage of flights severely
affected by metering is small, and that most of the flights receive less than a 10-minute gate-hold.
Although the percentage of flights held at the gate for more than ten minutes is small, those flights
that do sit at the gate longer represent an operational inconvenience for airlines. One reason is that the
Department of Transportation calculates on-time performance in terms of the pushback times from the gate,
and a flight is considered delayed if its actual pushback time exceeds its scheduled pushback time by more
than 15 minutes. This paper therefore considers variants of pushback rate control that can enable the benefits
of regulating departing demand, while minimizing the adverse impacts of long gate-holds on the airlines.
The three implementation variants considered are:
Policy 1: Departure metering with no limits on gate-holds, namely, the metering strategy previously
presented.
Policy 2: Metering with a maximum gate hold of 15 minutes. This policy keeps track of the duration of
gate holds for each flight and in those circumstances when a 15-minute wait is fulfilled, that flight pushes
back and starts the taxi-out process. This restricted version of metering avoids those situations represented
to the right of the dotted lines for 15 minutes in Figure 2, making them move exactly to the 15-minute mark.
The fifteen-minute restriction reduces the number of flights with long gate holds, which help the airline
operations on the ground as they become more predictable. There is, however, a cost associated to this
increase in predictability in terms of the taxi-out times. Indeed, as more aircraft pushback “unnecessarily”
– from the metering perspective – the surface traffic increases to levels above the saturation threshold, Ncontrol, and therefore, there are aircraft on the surface that do not contribute to departing throughput. Hence,
the taxi-out reductions are expected to be smaller than in the Policy 1 case.
Policy 3: Metering with a maximum gate hold of 10 minutes. This policy is equivalent to Policy 2, with a
10-minute limit, but it represents less of a change from the baseline case and it is expected to longer taxi-
Fornes, H. & Balakrishnan, H.
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out times. In this case, the restricted metering approach would lead to avoiding those flights to the right of
the dashed line in Figure 2.
These three implementation policies present an interesting range of metering alternatives to
evaluate.
RESULTS
Before proceeding to the simulation results, it is worth understanding the implications to the model
of each one of the implementation policies. Figure 3 provides the reader with a graphic representation of
the main conceptual outputs of the simulation as well as a visual representation of how such model outputs
vary by implementation policy.
Figure 3: Results of the model for July 9 2013. The first set of curves (top subplot) represent departure surface
traffic under different policies. The second set of curves (positive side of the bottom subplot) depict average
taxi-out time. The third set of curves (negative side of the bottom subplot) illustrates the average duration of
gate holds.
Figure 3 illustrates, by 15-minute period of a day, the departing surface traffic, the average taxi-out
time, and the gate-holding time for the baseline and the three policies. The first observation is the clear
correspondence between the three variables, as was expected. We note that there are no gate holds –
represented by gate holding time different from zero – unless the surface traffic is above the N-control
value, which varies by runway configuration and visibility conditions. For surface traffic values below Ncontrol, the three metering implementation policies track the baseline case. The surface traffic for any of
the three implementation policies is, as expected, always equal or smaller than the baseline case. As a
consequence, average taxi-out times are always equal or lower than the baseline case for every policy.
Furthermore, Policy 3 (by definition is the most similar to the baseline) leads to both surface traffic and
taxi-out times closer to those in the baseline; Policies 1 and 2 differ most significantly from the baseline,
but also experience shorter taxi-out times.
Figure 3 helps explain the conceptual relations between the departing surface traffic, the taxi-out
time, and the gate holding time. However, this figure is just a snapshot of one specific date, which may not
necessarily be representative of a set of days. Another aspect worth being cautious about while extrapolating
these figure results is the use of averages, particularly after knowing of the existence of long distribution
Fornes, H. & Balakrishnan, H.
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tails of up to 30 minutes of taxi-out time. Indeed, averages do a better job than medians to include outliers,
but there might still be a lack of representation of those extreme values.
In order to carry out sound comparisons between implementation policies, it is helpful to compare
the total taxi-out time reduction time by airline, the percentage taxi-out time reduction by airline, and the
percentage of the aggregate time reduction each airline takes advantage of; along with the total duration of
gate holding times by airline, as well as the percentage of the aggregate gate holding time that each airline
encounters. Table 1 presents all these indicators for simulations of the three different policies from July 1
2013 to August 31 2013, in addition to the share of departures corresponding to each airline.
Table 4: Simulation results by airline and by pushback policy.
Taxi-out time reductions
Airline
Policy
Minutes % reduction
% share
1
2
3
4
5
6
7
Others
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
18,299
13,659
11,724
8,894
6,599
5,702
4,520
3,260
2,844
1,792
1,313
1,116
794
594
527
1,956
1,364
1,158
1,066
710
594
9,849
7,447
6,497
4.9%
3.6%
3.1%
5.0%
3.7%
3.2%
5.3%
3.8%
3.4%
4.8%
3.5%
3.0%
4.6%
3.4%
3.1%
4.6%
3.2%
2.7%
4.2%
2.8%
2.3%
5.1%
3.9%
3.4%
38.8%
39.1%
38.9%
18.9%
18.9%
18.9%
9.6%
9.3%
9.4%
3.8%
3.8%
3.7%
1.7%
1.7%
1.7%
4.1%
3.9%
3.8%
2.3%
2.0%
2.0%
20.9%
21.3%
21.5%
Gate holding time
Minutes
% share
18,294
13,752
11,873
8,572
6,460
5,576
4,957
3,360
2,869
1,776
1,266
1,096
668
569
503
1,851
1,323
1,142
1,000
718
628
10,053
7497
6476
38.8%
39.4%
39.4%
18.2%
18.5%
18.5%
10.5%
9.6%
9.5%
3.8%
3.6%
3.6%
1.4%
1.6%
1.7%
3.9%
3.8%
3.8%
2.1%
2.1%
2.1%
21.3%
21.5%
21.5%
% share
of
departures
38.0%
19.8%
8.2%
4.3%
2.0%
5.1%
3.1%
19.6%
There are several observations worth noting. First, as expected, airlines obtain the most significant
taxi-out time reductions with Policy 3, at the cost of having the longest taxi-out times. Policy 1 leads to the
opposite results; this is valid for all airlines. The taxi-out time reductions from Policy 1 range between 4.2%
and 5.3%, those from Policy 2 are between 2.8% and 3.9%, and from Policy 3 are between 2.3% and 3.4%.
Second, one minute of gate-hold leads to approximately one minute reduction in taxi-out time. This
result is important because it ensures that no additional departure delay is added, and that instead of idling
on the taxiway, the waiting time occurs at the gate.
Third, the advantages (taxi-out reductions) and impacts (gate-holding times) of the model are
approximately distributed in a proportional way to the market share of each airline (in terms of the number
Fornes, H. & Balakrishnan, H.
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of operations). Additionally, these proportions are rather well-maintained for the three policies (+/- 2%),
illustrating the fairness of the approach.
CONCLUSIONS
This paper considers modifications to a pushback rate control strategy previously field-tested at
Boston, in order to ease the practical implementation challenges that arise at a more chronically congested
airport such as LGA.
Policy 1, which does not limit gate-hold times, achieves the largest reduction in taxi-out times, and
therefore fuel burn and emissions. In addition to this, fewer aircraft on the ground reduces the number of
interactions between arriving and departing aircraft on the surface, potentially improving safety. Airlines
will see fuel burn reduction from this type of implementation policy, but at the same time may be concerned
about operational challenges on the ramp due to lack of predictability. Policies 2 or 3 help alleviate some
of these practical challenges for airlines by limiting the gate hold times.
Simulations of all three variants and the baseline operations show that all three variants can be
implemented in such a manner that the gate-hold times are commensurate with the reductions in taxi-out
times. In addition, gate-holds and taxi-out time savings in all three variants are allocated in proportion to
the number of departure operations amongst the different air carriers, thereby ensuring fairness.
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