In this chapter, we studied the three prominent VMC runway configurations at EWR, and we found that the departure throughput of EWR is not sensitive to changes in runway configuration, arrival throughput and fleet mix.
We then extended the methodology developed in Chapter 2 to study interactions among the three major airports of the NY Metroplex, namely, JFK, EWR and LGA. We found that operations at the three airports are not adversely impacted by operations at the other airports, and we derived capacity envelopes for the system comprising the three airports under different configurations. We estimated that the total balanced operations capacity of the Metroplex is 59 AC/15 min, the departure priority capacity is 53 AC/15 min, and the arrival priority capacity is 63 AC/15 min.
We also identified opportunities for performance improvement.
We finally showed that information on route availability can be used for estimating the oper-ational throughput of an airport. We demonstrated that route availability explains a significant fraction of the variation of the departure throughput at LGA, when the prevailing conditions are VMC and the airport is in saturation. In Appendix D, we show that route availability informa-tion can be combined with fleet mix informainforma-tion for deriving parametrized operainforma-tional throughput envelopes for PHL.
Chapter 4
Queuing Model of the Departure Process
In this chapter, we develop an analytical queuing model of the departure process. We train this model using ASPM data from EWR, and evaluate it in terms of its ability to predict taxi-out times and the flow of aircraft on the airport surface. In contrast to Chapters 2 and 3, which focused on estimating the expected throughput under different conditions, this chapter focuses on the derivation of distributions of the random variables involved in the departure process, and the estimation of the impact of their variability on the taxi-out delays.
The main objective of this chapter is to develop a generalizable and easily adaptable model of departure operations. The model development is illustrated for the two main runway configurations of EWR in 2011. The model is also calibrated for PHL in Appendix G, and for CLT in Appendix H.
The model can be used for predicting aggregate taxi-out times and surface congestion, given a pushback schedule for a short, or long time horizon1. In this chapter we use the model, which is calibrated using 2011 data from EWR, to predict taxi-out times and surface congestion for departures at the two main runway configurations of EWR in 2007 and 2010. We show that the model can be used for tactical departure planning as well, that is, predicting taxi-out times and departure queues for a short time horizon, like a few hours, or a day. We also assess the impact of different pushback schedules on the variability of delays. Finally, we provide approximate estimates
1It is important to note that we do not investigate the impact of uncertainty in the pushback schedules in this work.
In other words, we study the predictive properties of the proposed models assuming that the pushback schedules are known. In the current system this may only be realistic for short time horizons (of about 15 minutes).
of the taxi-out times of individual flights.
In addition, the model can be used as a platform for developing and evaluating control al-gorithms for the departure process, as will be shown in Chapter 5. It is also suitable for policy analysis of infrastructure or operational changes, because of its analytical nature. We describe one such application in Appendix H, where the proposed model is used to assess the impacts of the new runway at CLT on the taxi-out times and the taxi-out delays at the airport.
4.1 Model inputs and outputs
The inputs to the model are
• Pushback schedule, P S.
• Airline of the departing flight, AL.
• Arrival throughput in a 15-minute period starting at time t, A(t).
• Route availability (in the airspace), if available, in a 15-minute period starting at time t, SRAP T(t).
• Segment in use, (M C; RC), expressed as the combination of the visibility conditions, M C, and the runway configuration, RC.
The outputs of the model are
• Number of departures (takeoffs) in the 15-minute period starting at time t, T (t).
• Total number of aircraft taxiing out at the beginning of period t, N (t). It indicates the congestion of taxiing out aircraft on the ground.
• Number of aircraft waiting in the departure queue at the beginning of period t, Q(t). The departure queue is defined as the queue which is formed at the threshold(s) of the departure runway(s), where the aircraft queue for takeoff.
• Number of departing aircraft traveling in the ramp and the taxiways towards the departure queue at the beginning of period t (i.e., the number of departures on the surface that have not reached the departure queue), R(t).
• Expected taxi time of departing aircraft l, E[τ (l)].
• Expected queuing delay that departing aircraft l experiences, E[Dl].
• Variance of the queuing delay that departing aircraft l experiences, var(Dl).
• Number of aircraft taking off between the pushback and takeoff time of aircraft l (the length of the takeoff queue experienced by aircraft l [58]), NQ(l).
• Runway schedule, RW . It refers to the times at which aircraft arrive at the departure queue.