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Runway Assignments That Minimize Terminal Airspace and Airport Surface Emissions

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Abstract

Air traffic operations at and around major airports in the United States are in need of improvement. From the perspective of arrivals, when one or more runways are in great demand, unnecessary delay and emissions occur during peak periods at major airports while other runways at the same airport are operating under capacity. The primary cause of this imbalance in runway utilization is that traffic flow into and out of terminal areas is asymmetric (as a result of airline scheduling practices) and arrivals are typically assigned to the runway nearest the fix through which they enter the terminal area. From the perspective of departures, delays and emissions are incurred because arrivals take precedence over departures with regard to the utilization of runways (despite the absence of binding safety constraints) and because arrival trajectories often include level segments that ensure "procedural separation" from arriving traffic while planes are not allowed to climb unrestricted along the most direct path to their destination. This paper presents an optimization model for simultaneously assigning aircraft to runways and scheduling the arrival and departure operations on these runways such that the total emissions produced in the terminal area and on the airport surface are minimized. Model constraints include such real-world considerations as the minimum separation required between successive aircraft and the precedence relationships that aircraft flying a common route take. An analysis of simulation results indicates that implementation of this model could result in as much as a 30% reduction in arrival emissions with a corresponding 3% reduction in departure emissions.

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... • Dynamic Programming, which was first proposed by (Dear, 1976) and (Psaraftis, 1978) followed by (Chandran and Balakrishnan, 2007;Lee and Balakrishnan, 2008;Balakrishnan and Chandran, 2010;Briskorn and Stolletz, 2013;Lieder et al., 2015) • Mixed-Integer Programming (MIP) preferred by (Beasley et al., 2000;Wen et al., 2005;Furini et al., 2012;Briskorn and Stolletz, 2013;Kim et al., 2014;Pohl et al., 2021) Heuristic and metaheuristic approaches represent other powerful methods for solving the ALP. While authors of (Bianco et al., 2006;Faye, 2015;Shi et al., 2019;Ikli et al., 2021b) make use of heuristics, others decide in favor of metaheuristic methods. ...
... In order to formulate a problem, which can be solved in reasonable time, it is essential to keep the number of binary variables as small as possible. In contrast to (Kim et al., 2014;Lieder et al., 2015;Ikli et al., 2021b), we have considerably less decision variables, namely the scheduling variables w ij 1 if aircraft i lands before aircraft j, 0 otherwise, and the allocation variables ...
... So far, many works as, e.g., (Kim et al., 2014;Lieder et al., 2015;Ikli et al., 2021b), have exploited an MIP-model with three sets of integer variables. For comparison, the exact model used by (Ikli et al., 2021b) can be found in Supplementary Appendix A. ...
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... This set of constraints is an either-type constraint. One way to convert either-type constraints to set of linear constraints is through the big-M method [20]. Similar to the approach used in Ref. [20], the separation constraint in ...
... One way to convert either-type constraints to set of linear constraints is through the big-M method [20]. Similar to the approach used in Ref. [20], the separation constraint in ...
... [8]), runway assignments and flight sequencing (e.g. [9], [10]) and taxiway guidance ( [11]). ...
... April [10][11][12]2018 ...
... [8]), runway assignments and flight sequencing (e.g. [9], [10]) and taxiway guidance ( [11]). ...
... April [10][11][12]2018 ...
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... Berge et al. (2006) presented a tool named Multiple Runway Planner (MRP) developed by Boeing for systematic analysis of arrival sequencing, scheduling and runway assignment with alternative performance objectives from airlines and ATC. Kim et al. (2014) presented an optimization model for simultaneously assigning aircraft to runways and scheduling the arrival and departure operations on these runways such that the total emissions produced in the terminal area and on the airport surface are minimized. Vela et al. (2015) studied the problem of strategically balancing departure demand at runways in order to reduce departure delays at airports with multi-runway configuration. ...
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... A closely related problem is runway assignment, which consists in assigning aircraft to runways and scheduling arrival and departure operations as to minimize delay or total emissions (Chandrasekar and Hwang, 2014;Vela et al., 2015;Kim et al., 2014;Liang et al., 2017). The resolution of this problem solves a tactical level issue, i.e., this problem must be solved every day by the airport. ...
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In this article, we present the development of a two-step optimization framework to deal with the design and selection of aircraft departure routes and the allocation of flights among these routes. The aim of the framework is to minimize cumulative noise annoyance and fuel burn. In the first step of the framework, multi-objective trajectory optimization is used to compute and store a set of routes that will serve as inputs in the second step. In the second step, the selection of routes from the set of pre-computed optimal routes and the optimal allocation of flights among these routes are conducted simultaneously. To validate the proposed framework, we also conduct an analysis involving an integrated (one-step) approach, in which both trajectory optimization and route allocation are formulated as a single optimization problem. A comparison of both approaches is then performed, and their advantages and disadvantages are identified. The performance and capabilities of the present framework are demonstrated using a case study at Amsterdam Airport Schiphol in The Netherlands. The numerical results show that the proposed framework can generate solutions which can achieve a reduction in the number of people annoyed of up to 31% and a reduction in fuel consumption of 7.3% relative to the reference case solution.
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... Chen and Zhao (2012) Recently, Guépet et al. (2017) solved the integrated runway sequencing and ground routing problem by heuristics. Other issues addressed with respect to the runway were emissions from flight delays (Kim et al., 2014) and runway operability at congested hub airports (Mori and Aoyama, 2015). The only issue in operation level studies for the terminal was the passengers' processing time. ...
... Chen and Zhao (2012) Recently, Guépet et al. (2017) solved the integrated runway sequencing and ground routing problem by heuristics. Other issues addressed with respect to the runway were emissions from flight delays (Kim et al., 2014) and runway operability at congested hub airports (Mori and Aoyama, 2015). The only issue in operation level studies for the terminal was the passengers' processing time. ...
... Discrete Dynamics in Nature and Society established a biobjective stochastic dynamic programming model to minimize GFI and maximize profits and obtained the optimal aircraft fleet composition. Kim et al. [35] combined with the minimum separation interval, aircraft priority order, operation path, and other factors to allocate the runway usage of the aircraft of the arrival and departure flights on the runway, so that the total emissions generated by the terminal area and the scene operation were minimized. Based on the input flight plan data (including operation, technology, and cost parameters, etc.), Rosskopf et al. [36] studied the balance between airline transportation cost and aircraft NO x emissions in the long-term fleet plan and found the Pareto optimal solution by changing the weight. ...
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Thesis
in Terminal Manoeuvring Area (TMA) at hub airports is the main problem in Chinese air transportation system. At most of the hub airports, the capacity is near saturated or even overloaded. Civil Aviation Administration of China (CAAC) reported that Air Traffic Management (ATM) is the main cause of delays. Despite the already overloaded ATM system, the Chinese airplanes fleet is continuing to expand. China will become the largest traffic flow in the world before the end of 2035. There is an urgent need to develop a new more efficient method for sequencing and merging arrival flows in TMA, so that airports can maximise the benefits from the emerging Communication, Navigation and Surveillance (CNS) techniques, and consequently increasing capacity. Automation can be highly efficient in ATM, however, due to safety considerations, full automation in ATM is still a challenge. 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It can realize a shorter flying time and a near-Continuous Descent Approach (CDA) descent for arrival aircraft, compared with baseline. For normal traffic, with near-equal traffic demand for two runways, with landing balancing function ON, the average flying time on different Standard Terminal ARrival (STAR) routes can be reduced up to 8 minutes compared with the baseline. It also realizes an easier re-sequencing of aircraft with more relaxed position shifting as well, compared with conventional sequencing method. Theoretically, the Maximum Position Shifting (MPS) can be up to 6 positions, overcoming the hard constraint of 3 position shifts (MPS <= 3). For asymmetric traffic, with big difference on traffic demand for two runways, with runway balancing function ON, it is more likely to find a conflict-free solution compared with the runway balancing function OFF, and again reduces the average flying time. Third, it is efficient for the segregated parallel approach patterns. Compared with hard constrained position shifting, which is often used in current Arrival Manager (AMAN) system and controller’s manual-control First Come First Served (FCFS) method, it can reduce the average delay, average additional transit time in super dense arrival situations. The average time flown level per flight is less than 12% of total transit time in TMA. Fourth, in independent parallel patterns, it can provide a range of useful information concerning the associated objective value, the average flying time, crossing trajectories in hot spots between arrivals and departures, the efficiency of using different designed sequencing legs in ML-PM route network. Thus, it helps the Air Navigation Service Provider (ANSP) to find the best configuration of ML-PM route network to efficiently satisfy the traffic demand. Last but not least, the computation time of our system is reasonable. It generally needs around 290s-350s for 2 hours of heavy traffic demand with the mixed parallel approach. In conclusion, theoretically, our system realizes good trajectory planning of dense flows at busy airports. It can guarantee a conflict-free solution, increase runway throughput, and minimize delay. At the same time, it can simplify merging, re-sequencing, and improve the economical descent profile with advanced ML-PM route design. Although the methodology defined here is illustrated using the BCIA airport, it could be easily applied to airports worldwide.
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Conference Paper
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Thesis
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With a significant impact on the development of the global economy and society, air transport is predicted to rapidly grow in the coming years. Unfortunately, while delivering positive global economic and social benefits, air transport has also generated an adverse influence on the environment, especially on the quality of life of communities around airports. Air transport is the main cause of noise nuisance in the vicinity of airports, which has been linked to various human health effects, such as cardiovascular diseases, sleep disturbance, hearing loss, and communication interference. Over the years, significant efforts have been put into finding suitable options to support the continued growth of air transport, such as the design of optimal routes and the optimal allocation of flights to specific routes and runways. However, research on these topics is typically conducted separately, and hence studies that consider the link between these topics are still lacking. In an attempt to fulfill the mentioned research gap, an optimization framework has been developed in this thesis. The developed framework aims to exploit the advantages of considering the route design and fight allocation optimization problems in a linked manner to minimize aircraft noise impact and fuel consumption simultaneously. The outcome of the research is a framework that is able to determine suitable routes for given standard departure instrument (SID) routes, and the optimal number of flights of each aircraft type that should be assigned to these routes, while taking operational constraints related to aircraft sequencing and separation requirements into account.
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This study estimates the potential benefits of continuous descents for more than 480,000 flights to 25 major airports in the National Airspace System. While reduced fuel consumption and greenhouse gas emissions are expected for these procedures, the benefits during periods of congestion are not well understood. To address this gap, baseline trajectories were constructed from flight plan and track data for flights arriving at 8 busy terminal areas. Two types of continuous descent trajectories were modeled. One enforced a constant distance-to-fly constraint to simulate uncongested operations. The other enforced a constant time-to-fly constraint to simulate congested operations. Potential fuel savings were calculated for different continuous descent scenarios. Analysis of the distance-constrained trajectories showed that fuel savings was distributed unevenly among the flights. The estimated savings was less than 25 kg for over 45% of the flights, and less than 100 kg for over 87% of the flights. The time-constrained trajectories showed 70-85% less potential savings than the distance-constrained trajectories. Prioritization of the improvements necessary to execute continuous descents during periods of congestion must rely upon analysis of a sufficient sample of operations, representative of many days, aircraft types, and traffic demand levels.
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This paper presents design principles and algorithm for building a real time scheduler. The primary objective of the scheduler is to assign arrival aircraft to a favorable landing runway and schedule them to land at times that minimize delays. A further objective of the scheduler is to allocate delays between high altitude airspace for from the airport and low altitude airspace near the airport. A method of delay allocation is described that minimizes the average operating cost in the presence of errors in controlling aircraft to a specified landing time.
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Accurate modeling of airplane fuel consumption is necessary for air transportation policy-makers to properly adjudicate trades between competing environmental and economic demands. Existing public models used for computing terminal-area airplane fuel consumption have been shown to have substantial errors, potentially leading to erroneous policy and investment decisions. The method of modeling fuel consumption proposed in this paper was developed using data from a major airplane manufacturer. When compared with airline performance/operational data, this proposed method has been shown to accurately predict fuel consumption in the terminal area. The proposed method uses airplane performance data from publicly available environmental models supported by the Federal Aviation Administration and others. The proposed method has sufficient generality to protect the proprietary interests of the manufacturer, while still having adequate fidelity to analyze low-speed airplane operations in the terminal area. This improved methodology will enable more informed decisions by policy-makers seeking to account for the effects of fuel consumption and airplane emissions on plans for future airspace and airport designs.
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Field observations at Boston Logan International airport and data analyses comparing Logan to other major airports are conducted in order to identify the flow constraints that impede departure operations in an airport system. These observations and the associated analyses are discussed for each of the components of the airport system. It is concluded that the airport systems is a complex interactive queuing system, that the different degrees, and that the runway system is the main flow constraint. The observations and analysis discussed reveal important implications for Departure Planning (DP) tools. The DP tools have competing objectives such as increasing the efficiency of the runway system, reducing delays and environmental impact, and maintaining fairness among all airport users and acceptable workload levels for air traffic controllers. The interactions and dynamics between the different components of the airport system determine how and where in the system the DP tools can reduce the delays and inefficiencies most effectively. Important interactions between the DP tools and other decision-aided tools such as the Center TRACON Automation System (CTAS) and the Surface Movement Advisor (SMA) are also discussed.
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Capacity constraints in the United States national airspace system (NAS) have coupled with the growth in air traffic to increase congestion in airways, terminal areas and on the airport surface. In this paper, we discuss the growth in aviation emissions from ground operations in the United States from 1995 to 2000 and investigate the potential for emissions reductions under 3 scenarios: improved operations, single-engine taxiing, and use of tow trucks to move aircraft to and from runways. Emissions estimates, unlike with previous models, are based on actual mission times and aircraft types from the Airline Service Quality Performance (ASQP) data, compiled by the US Department of Transportation (DOT) from information provided by the ten largest US carriers. Results indicate that surface emissions have been growing faster than airborne operations or total mission time in domestic US aviation, and may therefore become a constraint on airport expansion. The potential for emissions reductions through improved ground operations and single-engine taxiing is significant. The net environmental benefits from using tow trucks are unclear and should be investigated further.
Conference Paper
A stochastic runway planning model has been developed for scheduling of airport runway operations in the presence of uncertainty, where stochastic attributes include pushback delay, time spent on taxiway, and deviation from estimated arrival time. The runway planning problem is modeled as a process in two stages, where the first stage uses a two-stage stochastic program to find an aircraft weight-class sequence that maximizes throughput while simultaneously achieving a desirable sequence for the second planning stage without having exact information on the stochastic parameters. In the second planning stage, individual aircraft are assigned to the sequence after exact information becomes available. The computational study shows that under certain assumptions, if the schedule is dense enough, there is a potential benefit of using the stochastic runway planner over a first-come, first-served planning policy or a deterministic runway planner.
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As airport surface surveillance technologies develop, aircraft ground position information becomes more easily available and accurate. This paper provides a better understanding of the value of future surface surveillance systems where departures, and more specifically pushback times, will be optimized. It analytically quantifies the potential benefits yielded by providing surveillance information to the agent or system that is entrusted with tactically optimizing pushback clearances under nominal conditions. A stochastic model of surface operations is developed for single-ramp surface operations and calibrated to emulate departure surface operations at LaGuardia Airport. Two levels of information are examined within a tactically optimized collaborative decision-making framework. For each level, emissions, number of taxiing aircraft, and runway utilization rate are analyzed and compared with a simple threshold policy to evaluate surface surveillance information. Safety benefits, however, are not considered in this paper. It is estimated that optimally controlling pushback clearances from a single-ramp area using detailed surface surveillance information does not provide significant benefits when compared with controlling pushback clearances using a gate-holding policy based on the number of aircraft currently taxiing. However, when the runway is functioning at intermediate capacity (50%-72% runway utilization rates), e.g., under adverse weather conditions, surveillance information may improve optimization of departure operations. In such case, emissions and the number of taxiing aircraft are reduced by up to 6% when compared with the gate-holding policy and by up to 3% when compared with the performance of an intelligent operator with limited information.
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The basic objective of arrival sequencing and scheduling in air traffic control automation is to match traffic demand and airport capacity while minimizing delays. The principle underlying practical sequencing and scheduling algorithms currently in use Is referred to as first-come-first-served (FCFS). Although this principle generates fair schedules when delays must be absorbed, it does not take into account airline priorities among individual flights. The development of new scheduling techniques that consider priorities expressed by air carriers will reduce the economic impact of air traffic management (ATM) restrictions on the airlines. This will also lead to increased airline economic efficiency by affording airlines greater control over their individual arrival banks of aircraft. NASA is exploring the possibility of allowing airlines to express relative arrival priorities to ATM through the development of new sequencing and scheduling algorithms that take into account airline preferences. A method of scheduling a bank of arrival aircraft according to a preferred order of arrival instead of according to an FCFS sequence based on estimated time of arrival at the runway is investigated. past-time simulation is used to evaluate this scheduling method in terms of the algorithm's ability to produce a preferred order of arrival and In terms of its ability to minimize delays (scheduling efficiency). Results show that compared with FCFS scheduling, the alternative scheduling method is often successful in reducing deviations from the preferred bank arrival order while causing little or no increase in delays that must be absorbed.
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Air traffic management (ATM) must often place restrictions on arriving flights transitioning from en route airspace to highly congested terminal airspace. The restriction of arrival traffic, or arrival flow management, is performed without regard for the relative priority that airlines may be placing on individual flights. The development of new arrival how management techniques that consider priorities expressed by air carriers will reduce the economic impact of ATM restrictions and lead to increased airline economic efficiency by allowing airlines to have greater control over their individual arrival banks of aircraft. NASA is exploring the possibility of allowing airlines to express relative arrival priorities to ATM through the development of new sequencing and scheduling algorithms ;that take airline arrival preferences into account. This paper introduces the concept of "delay exchange" which is defined as a fair method of accommodating an airline request for an earlier arrival by advancing the landing time of one aircraft while simultaneously delaying another aircraft from the same airline. Fast-time simulation is used to evaluate the feasibility of scheduling these delay exchanges among individual arriving aircraft. Results show that the probability of successfully time advancing an aircraft is highest for an algorithm that allows delay exchange between aircraft arriving at any feeder fix. Results also show that the success of this algorithm varies with airport acceptance rate, indicating that the performance of this algorithm is a function of traffic density as well as the position of the aircraft within the traffic rush interval.
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Accurate and timely predictions of airline pushbacks can potentially lead to improved management of airport surface traffic, including reductions in the variability and average duration of costly airline delays. One factor which affects the realization of these benefits is the level of uncertainty inherent in the turn processes. Novel analyses of the minimum inherent uncertainty yield a significant lower bound on the predictability of airline pushbacks under the best possible conditions. These analyses are based on a large and detailed dataset of approximately 10 4 real-world turn operations obtained through collaboration with Deutsche Lufthansa AG. Three techniques are developed for predicting time-to-go until pushback as a function of available ground time; elapsed ground time; and the status (not started, in progress, or completed) of individual sub-processes in the turn such as catering, fueling, etc. This lower bound result shows that airport surface traffic management must incorporate robust mechanisms for coping with pushback demand stochasticity. These results also characterize the forecast horizon over which pushback predictions are accurate. Copyright © 2005 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
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An optimal airport arrival scheduling algorithm, which works within a hierarchical scheduling structure, is presented. This structure consists of schedulers at multiple points along the arrival route. Schedulers are linked through acceptance rate constraints, which are passed up from downstream metering points. The innovation in this algorithm is that these constraints are computed by using an Eulerian model-based optimization scheme. In this scheme an aggregate airspace model is derived online. Optimization based on predictions of this model is used to compute the optimal acceptance rates at all metering points, so that the airport throughput is maximized while keeping sector counts within limits. This rate computation removes inefficiencies introduced in the schedule through ad hoc acceptance rate computations. The scheduling process at every metering point uses its optimal acceptance rate as a constraint and computes optimal arrival sequences by using a combinatorial search algorithm. We test this algorithm in a dynamic air-traffic environment, which can be customized to emulate different arrival scenarios. Results from Monte Carlo simulations show improved performance over first-come, first-served ordering for different values of key parameters such as arrival aircraft mix, average arrival rate, maximum position-shift constraint, and level of uncertainty in estimated time of arrival prediction.
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We propose a job-shop scheduling model with sequence dependent set-up times and release dates to coordinate both inbound and outbound traffic flows on all the prefixed routes of an airport terminal area and all aircraft operations at the runway complex. The proposed model is suitable for representing several operational constraints (e.g., longitudinal and diagonal separations in specific airspace regions), and different runway configurations (e.g., crossing, parallel, with or without dependent approaches) in a uniform framework. The complexity and the highly dynamic nature of the problem call for heuristic approaches. We propose a fast dynamic local search heuristic algorithm for the job-shop model suitable for considering one of the different performance criteria and embedding aircraft position shifting control technique to limit the controllers/pilots’ workload. Finally, we describe in detail the experimental analysis of the proposed model and algorithm applied to two real case studies of Milan-Malpensa and Rome-Fiumicino airport terminal areas.
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The commercial aviation community has experienced a significant increase in the demand for air transportation over the past two decades. Despite the advent of new air traffic control (ATC) technologies, this increase in demand for air transportation has not been matched by an increase in capacity. Thus, because capacity/demand mismatches affect ATC airspace and ground operations, there has been a significant increase in delays during departure operations at major European and U.S. airports. The associated environmental impact and economic inefficiencies generate a growing need for the reduction of such delays. A framework and solution methodology developed at the Massachusetts Institute of Technology and the German Aerospace Research Establishment (DLR), to develop automated decision-aiding systems to assist air traffic controllers in handling departure traffic and mitigating the adverse effects of ground congestion and delays is presented. A possible formulation of the runway operations planning problem is presented with objectives and constraints.
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Flights incur a large percentage of their delays on the ground during the departure process between their scheduled departure from the gate and takeoff. Because of the large uncertainties associated with them, these delays are difficult to predict and account for, hindering the ability to effectively manage the Air Traffic Control (ATC) system. This paper presents an effort to improve the accuracy of estimating the taxi-out time, which is the duration between pushback and takeoff. The method was to identify the main factors that affect the taxi-out time and build an estimation model that takes the most important ones into account. An analysis conducted at Boston Logan International Airport identified the runway configuration, the airline/terminal, the downstream restrictions and the takeoff queue size as the main causal factors that affect the taxiout time. Of these factors the takeoff queue size was the most important one, where the queue size that an aircraft experienced was measured as the number of takeoffs that took place between its pushback time and its takeoff time. Consequently, a queuing model was built to estimate the taxi-out time at Logan Airport based on queue size estimation. For each aircraft, the queuing model assumes knowledge of the number of departure aircraft present on the airport surface at its pushback time and estimates its takeoff queue size by predicting the amount of passing that it may experience on the airport surface during its taxi out. The prediction performance of the queuing model was compared at Logan Airport to a running average model, which represents the baseline used currently in the Enhanced Traffic Management System (ETMS). The running average model uses a fourteen-day average as the estimate of the taxi-out time. The queuing model improved the mean absolute error in the taxi-out time estimation by approximately twenty percent and the accuracy rate by approximately ten percent, over the fourteen-day running average model.
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This report describes the development of a three-dimensional database of aircraft fuel burn and emissions (fuel burned, NOx, CO, and hydrocarbons) from scheduled commercial aircraft for each month of 1992. The seasonal variation in aircraft emissions was calculated for selected regions (global, North America, Europe, North Atlantic, and North Pacific). A series of parametric calculations were done to quantify the possible errors introduced from making approximations necessary to calculate the global emission inventory. The effects of wind, temperature, load factor, payload, and fuel tankering on fuel burn were evaluated to identify how they might affect the accuracy of aircraft emission inventories. These emissions inventories are available for use by atmospheric scientists conducting the Atmospheric Effects of Aviation Project (AEAP) modeling studies. Fuel burned and emissions of nitrogen oxides (NOx as N02), carbon monoxide, and hydrocarbons have been calculated on a 1 degree latitude x 1 degree longitude x 1 kilometer altitude grid and delivered to NASA as electronic files.
Conference Paper
A novel scheduling algorithm has been designed and coded to optimize arrival aircraft sequences and schedules. This algorithm, called the implicit enumeration (IE) scheduling algorithm, has been adopted as the foundation of the Traffic Management Advisor which is part of the Center/Terminal Radar Approach Control (TRACON) Automation System (CTAS). Because of the dynamic nature of air traffic control, the IE scheduling algorithm has been developed to operate in a dynamic feedback environment. The algorithm structure which controls the scheduling feedback also provides the platform for other advances. The criteria to be optimized can be specified flexibly, and can be varied on-line. Another feature of the algorithm applies to the situation found at many high-density airports, where multiple runways are used for arrivals simultaneously. The approach taken in the algorithm allows the planning process to be expanded to include runway assignment. Initial results from using the IE algorithm for runway assignment indicate that significant performance enhancements are possible when both runway and sequence assignments are considered in the scheduling process. This algorithm was first used in early 1992 at the Denver Air Route Traffic Control Center for the first stages of the field evaluation of CTAS by the FAA
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This paper formulates a new approach for improvement of air traffic flow management at airports, which leads to more efficient utilization of existing airport capacity to alleviate the consequences of congestion. A new model is presented, which first considers the runways and arrival and departure fixes jointly as a single system resource, and second considers arrivals and departures simultaneously as two interdependent processes. The model takes into account the interaction between runway capacity and capacities of fixes to optimize the traffic flow through the airport system. The effects are achieved by dynamic time-dependent allocation of airport capacity and flows between arrivals and departures coordinated with the operational constraints at runways and arrival and departure fixes as well as with dynamics of traffic demand and weather. Numerical examples illustrate the potential benefits of the approach
Aviation and the Global Atmosphere: A Special Report of the Intergovernmental Panel on Climate Change
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Use of Third-party Aircraft Performance Tools in the Development of the Aviation Environmental Design Tool (AEDT)," U.S. Dept. of Transportation's Volpe Center
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Dons, J., Mariens, J., and O'Callaghan, G. D., "Use of Third-party Aircraft Performance Tools in the Development of the Aviation Environmental Design Tool (AEDT)," U.S. Dept. of Transportation's Volpe Center, Rept. DOT-VNTSC-FAA-11-08, July 2011.
Free Route Airspace Project (FRAP)-Environmental Benefit Analysis
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Transport and Its Infrastructure in IPCC Fourth Assessment Report: Working Group III Report Mitigation of Climate Change
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Ribeiro, S. K., Kobayashi, S., Beuthe, M., Gasca, J., Greene, D., Lee, D. S., Muromachi, Y., Newton, P. J., Plotkin, S., Sperling, D., Wit, R., and Zhou, P. J., "Transport and Its Infrastructure in IPCC Fourth Assessment Report: Working Group III Report Mitigation of Climate Change," Intergovernmental Panel on Climate Change, Cambridge Univ. Press, Cambridge, England, U.K., 2007, p. 376.
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International Civil Aviation Organization
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Runway Operations Optimization in the Presence of Uncertainties
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