Queuing Models of Airport Departure Processes for Emissions Reduction

Abstract

Aircraft taxiing on the surface contribute significantly to the fuel burn and emissions at airports. This paper investigates the possibility of reducing fuel burn and emissions from surface operations through a reduction of the taxi times of departing aircraft. A novel approach is proposed that models the aircraft departure process as a queuing system, and attempts to reduce taxi times and emissions through improved queue management strategies.The departure taxi (taxi-out) time of an aircraft is represented as a sum of three components, namely, the unimpeded taxi-out time, the time spent in the departure queue, and the congestion delay due to ramp and taxiway interactions. The dependence of the taxi-out time on these factors is analyzed and modeled. The performance of the model is validated through a comparison of its predictions with observed data at Boston's Logan International Airport (BOS). The reductions in taxi-out times from the proposed queue management strategy are translated to reductions in fuel burn and emissions using ICAO engine models for the taxi phase of the flight profile. Nomenclature P SPushback schedule RC Runway configuration M C Meteorological conditions GL Gate location of a departing flight P (t) Number of aircraft pushing back during time period t N (t) Number of departing aircraft on the surface at the beginning of period t Q(t)Number of aircraft waiting in the departure queue at the beginning of period t R(t)Number of departures on the surface not at the departure queue at the beginning of period t C(t)Departure capacity of the departure runways during period t T (t) the number of takeoffs during period t N Q (i) Number of takeoffs between the pushback time and takeoff time of aircraft i τ (i)Taxi time of departing aircraft i τ unimped (i) Unimpeded taxi-out time of aircraft i τ taxiway (i) Delay to aircraft i due to aircraft interactions on the ramp and the taxiways τ dep.queue (i) Time aircraft i spends in the departure queue N * Saturation point for a segment N cntrlCritical N at which aircraft are held in the virtual departure queue

Network Congestion Control of Airport Surface Operations

“…[4][5][6][7] Since surface operations are highly heterogenous in terms of taxi speeds, routes and pilot behavior, Eulerian models 8,9 of traffic flow are not applicable here. Most traditional solution approaches involve the use of optimal scheduling algorithms 6,7 or queuing theory, [3][4][5] both of which have certain drawbacks. Optimization using linear or mixed-integer programming assumes that aircraft move at constant velocities at all times and follow time-based taxi instructions exactly, which are not realistic assumptions given the current state of technology at airports.…”

“…By contrast, this paper presents a formulation that explicitly accounts for stochastic taxi-out behavior, while providing insight into the system via an analytical treatment of airport performance. The approach proposed in this paper represents a mesoscopic model, which tries to strike a balance between macroscopic queuing models [2][3][4] and microscopic, aircraft trajectory-based ones. 6,7 To the best of our knowledge, this paper is the first to develop a model of airport operations using actual surface surveillance data.…”

Demonstration of reduced airport congestion through pushback rate control

“…The first step is to determine the unimpeded taxi-out times of flights using ASDE-X data, adopting a procedure similar to the one proposed by Simaiakis and Balakrishnan (2009). Given the pushback clearance time, the unimpeded taxi-out time is then used to propagate each flight to the runway, where it is matched to the next available departure slot for that time period, which determines the predicted wheels-off time.…”

“…The dependence of the departure throughput on the number of aircraft taxiing out and the arrival rate is illustrated for a particular runway configuration in Figure 1 using 2007 data from FAA's Aviation System Performance Metrics (ASPM) database. Beyond the threshold N * , any additional aircraft that pushback simply increase their taxi-out times without any increase in the departure throughput (Simaiakis and Balakrishnan, 2009). The value of N * depends on the airport, arrival demand, runway configuration, and meteorological conditions.…”

A combined statistical approach and ground movement model for improving taxi time estimations at airports

“…More recently, two further estimation approaches were published for North American airports. Simaiakis and Balakrishnan (2009) presented a queuing model and potential impact on emissions reduction. The statistical analysis exclusively used the size of the take-off queue to estimate the taxi-out time.…”