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Runway Assignments That Minimize Terminal Airspace and Airport Surface Emissions
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.
... • 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. ...
Due to the upward trend in the aviation industry, the existing approaches for air traffic control need to be improved to achieve efficient schedules. This paper deals with the aircraft landing problem, which consists of determining a landing time for each aircraft within the radar range of an airport and allocating it to a runway. We propose an exact solution approach that involves mixed-integer linear programming. The objective is hereby to minimize the sum of weighted deviations from the target landing times under consideration of different safety, efficiency and fairness constraints. Despite of the problem’s NP-hardness, our method exhibits low execution times thanks to a modified modeling strategy and provides near-optimal results. Numerical experiments prove efficiency of the approach for different large airports.
... Combined optimization is especially crucial for interdependent and heterogeneous runways where the operations on one runway pose restrictions on the other(s) (Lieder and Stolletz, 2016). Recently, a few studies have explored the CTLP while taking environmental concerns into account (Kim et al., 2010(Kim et al., , 2014Sölveling et al., 2011;Yin et al., 2021;Abbenhuis and Roling, 2022). Kim et al. (2010Kim et al. ( , 2014 proposed joint optimization models for runway scheduling of landing and take-off aircraft. ...
... Recently, a few studies have explored the CTLP while taking environmental concerns into account (Kim et al., 2010(Kim et al., , 2014Sölveling et al., 2011;Yin et al., 2021;Abbenhuis and Roling, 2022). Kim et al. (2010Kim et al. ( , 2014 proposed joint optimization models for runway scheduling of landing and take-off aircraft. These models minimize the pure total emissions, which are the sum of emissions for arrivals in the terminal airspace and on the airport ground. ...
The escalating demand for air travel has significant environmental implications, such as air pollution and noise nuisance, compelling airports to mitigate their negative impacts on the environment. Operations Research (OR) has played a crucial role in optimizing environment-oriented airport traffic flow management problems, including gate assignment, pushback control, taxi planning, tow vehicles operations, runway scheduling, and aircraft trajectory optimization, to align with airports’ environmental objectives. Despite the thriving body of papers, there is a lack of an inclusive review framework for papers in this field. Therefore, this paper introduces a comprehensive classification framework and conducts a thorough review of
the application of OR in addressing environmental concerns. The review identifies prevailing trends, summarizes environmental concerns and methodologies, and presents research agendas. Future studies should prioritize efforts to enhance existing research in terms of modeling and algorithms while also exploring the interdisciplinary incorporation of OR with reinforcement learning and big data analytics, as well as its application in emerging scenarios.
... In the optimization problem, various objective functions are considered depending on the decision makers (airport and airline operators, and governments), with most studies generally aiming to increase runway capacity from the airport's perspective and maximizing on-time performance and minimizing fuel consumption from the airline's perspective. The exact solution approach including mixed-integer programming [13], [14] and dynamic programming [15], [16] can obtain optimal solutions within a short computation time while flexibly incorporating various operational constraints and separation requirements on multiple runways. Conversely, the stochastic optimization methods, such as genetic algorithm [17], [18], simulated annealing [19], [20], tabu search [21], [22], and ant colony optimization [23], [24], can update solutions according to the dynamic changes in the schedule within a short computation time. ...
... Even with the extension of flight time, the effect of GtG arr reduction in 13 d has been recognized, so this operation is expected to improve punctuality compared with the current situation. For departing aircraft, T X dep is considerably reduced during the 5-d period only ( Days 2,5,13,14,and 20), indicating that the effect of the runway reassignment is limited. However, there are no considerably worse days, and the averaged median and maximum T X dep were reduced by 0.05 min (3 s) and 0.18 min (10.8 s), respectively. ...
There are ongoing research efforts to implement En route arrival manager (AMAN), which decides arrival runways and controls cruise speed in en route airspace. Air traffic control operations that regulate arrival air traffic flows from en route airspace are considered effective in mitigating the congestion close to destination airports. Therefore, this study proposes a scientific system design for operationally feasible En Route AMAN assisting air traffic controllers (ATCos) through runway-flow and inter-aircraft control. Herein, we devise an airline-oriented runway assignment rule that selects a target minimizing arrival taxi time in case of over-demand according to the maximum estimated through the stochastic distribution of inter-aircraft time and runway occupancy time. We also formulate speed control rules based on inter-aircraft spacing using simulation-based optimization and decision tree analysis to visualize the distinct strategies and rules for the traffic responsible for each ATCo. Furthermore, an agent-based simulation is performed to evaluate the system effectiveness in reducing the arrival delay. The simulation indicates 20-d arrival and departure at the Tokyo International Airport, Japan, between 06:00 and 23:00. The results show that the designed IF–THEN rules reduce the total arrival sequencing delay time and arrival taxi time by 21% (median, 55.8 s) and 6.9% (median, 24.6 s). Our findings suggest that truly optimal scheduled time of arrival (STA) and operationally feasible rules for ATCos could promise congestion relief while ensuring the interpretability and possibility of En Route AMAN implementation.
... They maximized both airport throughput and flight quality of service by considering uncertainties in flight and taxi times. Kim et al. considered HC, CO, and NOx emissions in their model of runway assignment and arrival/departure scheduling [18]. Vela et al. proposed strategic and tactical departure runway balancing methodologies and analyzed the benefits in throughput and delay [19]. ...
... Using these orders, the CPS constraints are given in Eqs. (17) and (18): the scheduled order of each aircraft at the corresponding runway should not be different from the original (i.e., FCFS-based) order by the CPS. ...
Airport runway operations are managed hierarchically. Initially, a runway configuration is determined several hours before the commencement of actual runway operations. Thereafter, each aircraft is assigned to a runway based on the runway configuration. Finally (or in certain instances, synchronous with runway assignments), runway times are scheduled for the runway assignment. Though typical runway operations management is carried out sequentially, runway configuration, runway assignment, and runway times are highly correlated. Hence, to the greatest extent practicable, runway operations should be managed both strategically (e.g., runway configuration determination) and tactically (e.g., runway assignment and runway time scheduling). As certain minor strategic changes, such as switching runway operation mode, can be implemented with a relatively short lead time, this study proposes a pretactical scheduling optimization problem of runway operations, especially for airports with multiple runways. The proposed scheduling method determines runway operation mode, runway assignments, and runway times concurrently. A numerical example of Incheon International Airport demonstrates that pretactical management can improve runway utilization and operational efficiency by reducing the disparity between active runways and runway queue delays.
... Alternatively, to improve operational efficiency at airports, significant efforts have been dedicated to mitigating congestion by optimizing the various components of airport operations. Existing research and practice have focused on gate/apron assignment (Dorndorf et al., 2017;Urszula and Atkin, 2013;Benlic et al., 2017), taxiing management (Marin, 2006;Pavese et al., 2017), and runway operation (EUROCONTROL, 2017;Kim et al., 2014). ...
... While gate assignment has been studied in existing literature, it is driven by the need to minimize conflicts within the apron area (Castaing et al., 2016;Dorndorf et al., 2012), or conceived from the passengers' perspective by minimizing the total walking distance and number of unaccommodated passengers in airport terminals (Yu et al., 2016;Zhang and Klabjan, 2017). Runway assignment, on the other hand, has mainly focused on runway sequencing and assignment (Kim et al., 2014;Vela et al., 2015). In summary, no studies have considered the joint assignment of apron and runway from the perspective of surface network optimization, with an objective to reduce taxiing distance and delays not only at these local points but also throughout the taxiway network. ...
Airport surface operations, such as off-block control, taxi routing, and runway sequencing, are typically confined to a fixed network topology with given origin–destination (O-D) pairs for departures and arrivals. Reconfiguring the O-D distribution of flights by actively assigning their aprons and runways is a potentially effective measure to maximize the utilization of network capacity. To date this has not been investigated in the literature due to its complexity involving operational constraints, assignment rules, and different stakeholders. This paper demonstrates the significant potential of O-D reconfiguration in improving surface network efficiency by proposing a joint apron-runway assignment framework for pre-tactical operations. This is underpinned by a comprehensive review of apron and runway assignment rules, including constraints and preferences, and an elaborated optimization scheme that encompasses lexicographic and iterative approaches along with temporal buffers to absorb uncertainties in pre-tactical operations. The proposed apron-runway assignment is implemented and assessed in a case study at Beijing Capital International Airport. An airport cellular automata simulator is employed for quantitative evaluation, and qualitative assessment is based on interviews with subject matter experts. Compared to the current operations, the proposed apron-runway assignment is very promising, with reductions in total taxiing distance, average taxiing time, taxiing conflicts, runway queuing time, and fuel consumption by 15.5% (Table 3), 6.2%, 19.8%, 17.6% (Table 4) and 6.6% (Table 5) respectively; gated assignment is increased by 11.8% (Table 4). Importantly, these benefits remain robust to unforeseen flight delays, as demonstrated in a sensitivity analysis.
... [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 ...
... 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 ...
... With taxi capacity [13] or conflict constraints, pushback delays were minimized, and runway throughputs are maximized in the optimization [14]. However, previous research mostly assumed a fixed runway selection and seldom focused on runway assignment problems [15,16]. ...
Collaboration between terminal airspace and airport surface operation shows an increasing significance for the best efficiency of both parts of the air traffic management domain. Runways play a critical role in connecting the two parts for departure and arrival aircraft. Suppose the gate and the entry fix of an aircraft are predetermined according to the flight plan, and they are on the opposite side of the airport terminal. The aircraft will either spend more time (i.e., delay in the air) landing on a runway close to its gate or take a longer distance (i.e., detour on the ground) taxiing to its gate if a runway close to its entry fix is assigned. This paper proposes a runway assignment model considering terminal airspace operation and airport surface movement simultaneously to discover how runway assignments can affect integrated operations. Four different runway assignment schemes are applied in this model. Subsequently, a metaheuristic method is proposed to solve the model. Furthermore, the historical taxiing and flight time data are analyzed to demonstrate the potential benefits of runway reassignment. Finally, the results show that the free assignment of the runway stands out among the four schemes, not only in the performance of terminal airspace operation (lower flight time) but also in airport surface movement (lower pushback delay, taxi time).
... Kim et al. [43] propose a novel MIP approach to include -additionally to runways -terminal-area entry fixes. Their model computes the runway and TRACON-fixe 5 assignment of each aircraft, as well as a runway schedule for landings, that minimizes total emissions in the TRACON, including airport surface emissions. ...
The aircraft scheduling problem consists in sequencing aircraft on airport runways and in scheduling their times of operations taking into consideration several operational constraints. It is known to be an NP-hard problem, an ongoing challenge for both researchers and air traffic controllers.
The aim of this paper is to present a focused review on the most relevant techniques in the recent literature (since 2010) on the aircraft runway scheduling problem, including exact approaches such as mixed-integer programming and dynamic programming, metaheuristics, and novel approaches based on reinforcement learning. Since the benchmark instances used in the literature are easily solved by high-performance computers and current versions of solvers, we propose a new data set with challenging realistic problems constructed from real-world air traffic.
... Bennell et al. (2013) review the techniques and tools of operational research and management science that are used for scheduling aircraft landings and take-offs. Kim et al. (2014) present an optimisation model for simultaneously assigning aircraft to runways and scheduling the arrival and departure operations on these runways. Chandrasekar and Hwang (2014) propose a framework to compute, with computational efficiency, the optimal runway assignment and sequencing of arrival and departure operations at an airport. ...
Incoming air traffic in a given airport can be provided by a great diversity of air routes. However, airports comprise a limited number of runways. The reduction in the number of paths that aircraft can transit takes place in terminal arrival routes, which act as an interface between incoming routes and approach trajectories. This occurrence entails that air traffic has to be carefully managed in terminal arrival routes in order to prevent possible bottlenecks. This work presents an optimisation model that manages not only approach and landing operations, but also terminal arrival routes, deciding on runway assignment, terminal arrival route selection and aircraft trajectory. The proposed integrated model leads to a mixed integer non-linear problem. For its resolution, a Benders decomposition is proposed. On the one hand, the master model deals with runway assignment and terminal arrival route selection, making use of a set of binary variables. On the other hand, the sub-model deals with the trajectory calculation problem, managing a set of continuous variables and minimising a combination of fuel consumption and delay.
... 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. ...
The hub-and-spoke network has demonstrated its economies of scale and scope in the rapid development of the civil aviation industry. In order to fit the development trend of green civil aviation, a series of environmental problems such as fuel consumption and pollutant emissions caused by air transportation cannot be ignored. Firstly, this paper selects six cities of Shenyang, Beijing, Qingdao, Zhengzhou, Guangzhou, and Nanjing as the research objects, collects the passenger flow and the distance information of the corresponding segment, determines the location of the hub airport, analyzes the operating environment of the aircraft in the hub-and-spoke network, establishes an aircraft emission assessment model, and calculates the mass of aircraft emissions and fuel consumption. Secondly, based on the calculation results, the comparison of aircraft emissions and fuel consumption between the hub-and-spoke network and the point-to-point network shows that the total carbon monoxide (CO) emissions are reduced by 35.84%, the total hydrocarbon compounds (HC) emissions are increased by 68.82%, and the total nitrogen oxides (NOx) emissions are increased by 24.87%. The total mass of pollutants (including CO, HC, and NOx) decreased by 29.37%, and the total fuel consumption decreased by 68.17%. In general, the use of a hub-and-spoke network reduces the pollutant emissions and fuel consumption of aircraft as a whole while ensuring the lowest passenger transportation cost. Finally, based on the current international situation and the enhancement of people’s awareness of environmental protection, a summary analysis of the hub-and-spoke network and the point-to-point network is obtained, and some enlightenment and research significance are obtained.
... 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. ...
... Kuiper et al. (2012) maximized the number of aircraft movements operating at an airport within an allotted annual noise budget by optimally assigning annual flights to available routes and runways. Kim et al. (2014) minimized airport surface emissions by concurrently allocating aircraft among runways and scheduling departure and arrival flights on these runways. Zachary et al. (2010) formulated and solved an optimization problem to minimize noise and pollutant emissions by simultaneously considering operational procedures, arrival and departure routes, and fleet combination. ...
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 to 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.
... Kuiper et al. (2012) maximized the number of aircraft movements operating at an airport within an allotted annual noise budget by optimally assigning annual flights to available routes and runways. Kim et al. (2014) minimized airport surface emissions by concurrently allocating aircraft among runways and scheduling departure and arrival flights on these runways. Zachary et al. (2010) formulated and solved an optimization problem to minimize noise and pollutant emissions by simultaneously considering operational procedures, arrival and departure routes, and fleet combination. ...
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.
... Ho- Huu et al. (2017) employ a multi-objective evolutionary algorithm based on decomposition to design optimal departure routes by minimizing noise and fuel consumption. In a related study, Kim et al. (2014) develop a mixed integer-programming model to schedule the departure and arrival operations on the runways by minimizing total emissions produced on the airport surface and in the terminal area. Ho- Huu et al. (2018) propose a model for the design of aircraft departure trajectories with objectives of minimizing the fuel consumption level, noise level and the number of annoyed people. ...
... 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. ...
Aircraft noise is recognized as a serious environmental concern and creates significant political problems. This paper presents a new mathematical model to deal with the aircraft noise in the vicinity of airports. The problem to be solved is defined as an assignment of aircraft operations over the routes in order to minimize the total annoyance to citizens at the same time as respecting a fairness scheme. A social-welfare perspective on the noise distribution problem is taken, in contrast to more traditional acoustics-based approaches.
Starting from the so-called
-fairness Social Welfare Function (SWF) used for maximization problems, we have defined a new -fairness function for minimization problems. We show that this function has similar properties to the maximization
-fairness SWF. Analytic results (optimal air traffic assignment, bounds for the Price of Fairness) are provided for the basic case i.e. when routes do not intersect. For more complex cases, when routes cross each others, we show, using a case study (the Brussels Airports case), that modern solvers are able to solve the problem to optimality for realistic scenarios.
The model can be used as a decision support tool for a Central Decision Maker (CDM) to allocate flight operations (take-off and landing) over the routes and establish policies in the vicinity of the airport. The paper also provides insights into understanding the relationship between efficiency and fairness.
... The theoretical analysis suggests that speed profile has an impact on the fuel consumption as much as, if not more than, vertical profile in the terminal area. Kim et al. (2014) present an optimization model for simultaneously assigning aircraft to runways and scheduling the arrival and departure operations on those runways, to minimize the total emissions produced in the terminal area and on the airport surface. ...
The rapid growth in air traffic has resulted in increased emission and noise levels in terminal areas, which brings negative environmental impact to surrounding areas. This study aims to optimize terminal area operations by taking into account environmental constraints pertaining to emission and noise. A multi-objective terminal area resource allocation problem is formulated by employing the arrival fix allocation (AFA) problem, while minimizing aircraft holding time, emission, and noise. The NSGA-II algorithm is employed to find the optimal assignment of terminal fixes with given demand input and environmental considerations, by incorporating the continuous descent approach (CDA). A case study of the Shanghai terminal area yields the following results: (1) Compared with existing arrival fix locations and the first-come-first-serve (FCFS) strategy, the AFA reduces emissions by 19.6%, and the areas impacted by noise by 16.4%. AFA and CDA combined reduce the emissions by 28% and noise by 38.1%; (2) Flight delays caused by the imbalance of demand and supply can be reduced by 72% (AFA) and 81% (AFA and CDA) respectively, compared with the FCFS strategy. The study demonstrates the feasibility of the proposed optimization framework to reduce the environmental impact in terminal areas while improving the operational efficiency, as well as its potential to underpin sustainable air traffic management.
... [10]), runway assignments and flight sequencing (e.g. [11], [12]) and taxiway guidance (e.g. [13]). ...
... The theoretical analysis suggests that speed profile has an impact on the fuel consumption as much as, if not more than, vertical profile in the terminal area. Kim et al. (2014) present an optimization model for simultaneously assigning aircraft to runways and scheduling the arrival and departure operations on those runways, to minimize the total emissions produced in the terminal area and on the airport surface. ...
The emission and contrail of flights at high altitude sector have a great impact on the environment. The paper establishes the multi-objective optimization method of high altitude sector operation based on environmental protection, which can reduce the impact of sector air traffic operation on the environment and enhance the operation efficiency of the high altitude sector. According to the operation characteristics of the sector, the aviation meteorological data of the sector are analyzed, and the forming conditions of the contrail are calculated. Considering the air traffic control rules and aircraft flight characteristics, a multi-objective optimization model of sector flight allocation strategy is established, and the influence of aviation meteorological conditions and different allocation strategy combinations on the optimal operation of sector is analyzed. To solve the multi-objective optimization model, the paper uses an evolutionary computation method in intelligence computing and takes non-dominated sorted genetic algorithm-II with elitist strategy (NSGA-II). Shanghai Area No.20 sector (ZSSSAR20) is selected as the case in this paper. Example verification reveals that taking number of contrails, fuel consumption and flight delays as the optimization targets, aircraft are more likely to rely on altitude deployment strategy so as to better reduce the impact of air traffic operation en route on environment, and improve sector operation efficiency en route.
... 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. ...
Congestion in Terminal Maneuvering Area (TMA) in hub airports is the main problem in Chinese air transportation. In this paper we propose a new system to integrated sequence and merge aircraft to parallel runways at Beijing Capital International Airport (BCIA). This system is based on the advanced avionics capabilities. Our methodology integrates a Multi-Level Point Merge (ML-PM) system, an economical descent approaches procedure, and a tailored heuristic algorithm to find a good, systematic, operationally-acceptable solution. First, Receding Horizontal Control (RHC) technique is applied to divide the entire 24 h of traffic into several sub-problems. Then in each sub-problem, it is optimized on given objectives (conflict, deviation from Estimated Time of Arrival (ETA) on the runway and makespan of the arrival flow). Four decision variables are designed to control the trajectory: the entry time, the entry speed, the turning time on the sequencing leg, and the landing runway allocation. Based on these variables, the real time trajectories are generated by the simulation module. Simulated Annealing (SA) algorithm is used to search the best solution for aircraft to execute. Finally, the conflict-free, least-delay, and user-preferred trajectories from the entry point of TMA to the landing runway are defined. Numerical results show that our optimization system has very stable de-conflict performance to handle continuously dense arrivals in transition airspace. It can also provide the decision support to assist flow controllers to handle the asymmetric arrival flows on different runways with less fuel consumption, and to assist tactical controllers to easily re-sequence aircraft with more relaxed position shifting. Moreover, our system can provide the fuel consumption prediction, and runway assignment information to assist airport and airlines managers for optimal decision making. Theoretically, it realizes an automated, cooperative and green control of routine arrival flows. Although the methodology defined here is applied to the airport BCIA, it could also be applied to other airports in the world.
View Video Presentation: https://doi.org/10.2514/6.2022-3709.vid In this paper, the authors seek to identify potential inefficiencies at airports controlled by local tower controllers when sequencing aircraft. Using naturalistic data in the form of historical air traffic radar data, we create and evaluate statistical and machine learning models of air traffic controllers when ordering aircraft to land at the airport. The models are based on binary decision classifiers (e.g., logistic regression) that take as their input static information related to the entrance times of aircraft, their traffic flow pattern, and dynamic state information (position, speed, etc) in order to predict a relative ordering. Once the relative ordering between all aircraft is established, a landing sequence is induced. A comparison of model predictions against the actualized decisions by air traffic controllers indicates that it is unlikely that air traffic controllers are making use of additional state information beyond an initial entry time and estimated landing time for each aircraft when setting a landing sequence. Furthermore, when performing the comparison on a set of historical trajectories at Ronald Reagan Washington National Airport (DCA) we demonstrate that mismatches between actualized decisions and the prediction models is associated with excess distance traversed by the aircraft, as such it appears there is room for additional sequence optimization.
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.
Spacing on the final approach segment while landing at an airport is critical to safe and efficient operations. Spacing on this segment is subject to a large number of stochastic factors that affect the ability of pairs of landing flights to achieve and maintain a desired separation. Due to the combinatorics of these stochastic factors, rule‐based automation cannot meet the performance requirements or adapt as the system characteristics migrate over time. Previous research described the design of a closed‐loop stochastic control system for an autonomous approach and landing system (A²LS) to manage spacing requirements to achieve safety and efficiency goals.
This paper describes the design of a model‐based simulation used to test the A²LS concept‐of‐operations. The simulation includes a model of the sequence of flights on the approach, the flow spacing closed‐loop control law for the A²LS, and a user‐interface to adjust plant and control law parameters and visualize the results. The limitations and implications of the simulation on the Model‐based System Engineering process are discussed.
This paper presents a new multi-objective optimization formulation for the design and allocation of optimal aircraft departure routes. In the considered problem-besides two conventional objectives based on cumulative noise criteria and fuel burn-a new objective considering the flight frequency is introduced. Moreover, to take advantage of the combination of designing new routes and allocating flights to these routes, two different routes are considered simultaneously, and the distribution of flights over these two routes is addressed in parallel. Then, a new version of the so-called MOEA/D optimization algorithm is developed to solve the formulated optimization problem. Two different case studies, one at Rotterdam The Hague Airport and one at Amsterdam Airport Schiphol in The Netherlands, are carried out to evaluate the reliability and applicability of the proposed approach. The obtained results reveal that the proposed approach can provide solutions which can balance more effectively the concerned metrics such as the number of annoyed people, fuel burn, number of people exposed to certain noise levels, and number of aircraft movements which people are subjected to.
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. Facing extremely dense operations in complex TMA, we can consider reducing traffic complexity by solving all potential conflicts in advance with a feasible trajectory control for controllers, or automating a large proportion of routine operations, such as sequencing, merging and spacing. As parallel runways are a common structure of Chinese hub airports, in this thesis, we propose a novel system to integrated sequencing and merging aircraft to parallel runways. Our methodology integrates a Area Navigation (RNAV)-based 3D Multi-Level and Multi-Point Merge System (MLMPMS), a hybrid heuristic optimization algorithm and a simulation module to find good, systematic, operationally-acceptable solutions. First, a Receding Horizon Control (RHC) technique is applied to divide 24-hour traffic optimization problem into several sub-problems. Then, in each sub-problem, a tailored Simulated Annealing (SA) algorithm and a trajectory generation module worn together to find a near-optimal solution. Our primary objective is to rapidly generate conflict-free and economical trajectories with easy, flexible and feasible control methods. Based on an initial solution, we continuously explore possible good solutions with less delay and shorter landing interval on runway. Taking Beijing Capital International Airport (BCIA) as a case to study, numerical results show that our optimization system performs well. First, it has very stable de-conflict performance to handle continuously dense traffic flows. Compared with Hill Climbing (HC), the tailored SA algorithm can always guarantee a conflict-free solution not only for the mixed iii or segregated parallel approach (arrivals only) pattern, but also for the independent parallel operation (integrated departures and arrivals) pattern. Second, with its unique Multi-Level Point Merge (ML-PM) route network, it can provide a good trajectory control solution to efficiently and economically handle different kinds of arrival flows. 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.
Continuous descent operations with controlled times of arrival at one or several metering fixes could enable environmentally friendly procedures without compromising terminal airspace capacity. This paper focuses on controlled time of arrival updates once the descent has been already initiated, assessing the feasible time window (and associated fuel consumption) of continuous descent operations requiring neither thrust nor speed-brake usage along the whole descent (i.e. only elevator control is used to achieve different metering times). Based on previous works, an optimal control problem is formulated and numerically solved. The earliest and latest times of arrival at the initial approach fix have been computed for the descent of an Airbus A320 under different scenarios, considering the potential altitudes and distances to go when receiving the controlled time of arrival update. The effects of the aircraft mass, initial speed, longitudinal wind and position of the initial approach fix on the time window have been also investigated. Results show that time windows about three minutes could be achieved for certain conditions, and that there is a trade-off between robustness facing controlled time of arrival updates during the descent and fuel consumption. Interestingly, minimum fuel trajectories almost correspond to those of minimum time.
With the rapid development of air transport industry, the phenomenon of air traffic congestion is becoming more and more serious. The research of air traffic congestion is a hot topic of the international civil aviation community, among which, the identification and prediction of air traffic congestion is the most important. The research on identification and prediction methods of air traffic congestion is generalized. Firstly, the research findings of the concept of air traffic congestion based on congestion formations and congestion aftereffects are summed up. Secondly, the important research methods of air traffic congestion identification based on different time scales of traffic data are reviewed. They are threshold identification based on short term data, clustering identification based on long term data and comprehensive evaluation based on mixed data. Thirdly, the air traffic congestion prediction methods based on mathematical algorithms (prediction based on mathematical statistics, traffic flow models and intelligent algorithms) and computer simulation techniques are summarized. Lastly, the recent research focus and the future research directions of identification and prediction of air traftic congestion are put forward. ©, 2015, AAAS Press of Chinese Society of Aeronautics and Astronautics. All right reserved.
Terminal area is very important airspace resource due to the limited capacity. Because several arrival traffics from different origin airport merge at arrival fix, the queue for arrival stream generally is formed at arrival fix and the inherent delay is propagated further out. Moreover it leads that arrivals do path stretching and speed adjustment to absorb that delay with consuming much more fuel than it was required. However this developed delay is often not distributed evenly, therefore some fixes experience unbearable delays while other fixes might idle. In this paper, we suggest a new approach to assign fix to resolve this issue while minimizing total consumed fuel and environmental impact in terminal airspace. The proposed model assigns arrival and departure fix for each arrival and departure respectively, considering the fix and runway capacity constraints. The proposed optimization model is beneficial to improve terminal area efficiency and local environment. In addition, this model functions more effectively at severe weather condition when existing unbalanced traffic demand.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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
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