Airport capacity increase via the use of braking profiles

Many airports are encountering the problem of insufficient capacity, which is particularly severe in periods of increased traffic. A large number of elements influence airport capacity, but one of the most important is runway occupancy time. This time depends on many factors, including how the landing roll procedure is performed. The procedure usually does not include the objective to minimize the runway occupancy time. This paper presents an analysis which shows that the way of braking during landing roll has an essential impact on runway throughput and thus on airport capacity. For this purpose, the landing roll simulator (named ACPENSIM) was created. It uses Petri nets and is a convenient tool for dynamic analysis of aircraft movement on the runway with given input parameters and a predetermined runway exit. Simulation experiments allowed to create a set of nominal braking profiles that have different objective functions: minimizing the runway occupancy time, minimizing noise, minimizing tire wear, maximizing passenger comfort and maximizing airport capacity as a whole. The experiments show that there is great potential to increase airport capacity by optimizing the braking procedure. It has been shown that by using the proposed braking profiles it is possible to reduce the runway occupancy time even by 50%.     

A fuzzy approach to addressing uncertainty in Airport Ground Movement optimisation

Allocating efficient routes to taxiing aircraft, known as the Ground Movement problem, is increasingly important as air traffic levels continue to increase. If taxiways cannot be reliably traversed quickly, aircraft can miss valuable assigned slots at the runway or can waste fuel waiting for other aircraft to clear. Efficient algorithms for this problem have been proposed, but little work has considered the uncertainties inherent in the domain. This paper proposes an adaptive Mamdani fuzzy rule based system to estimate taxi times and their uncertainties. Furthermore, the existing Quickest Path Problem with Time Windows (QPPTW) algorithm is adapted to use fuzzy taxi time estimates. Experiments with simulated taxi movements at Manchester Airport, the third-busiest in the UK, show the new approach produces routes that are more robust, reducing delays due to uncertain taxi times by 10–20% over the original QPPTW.     

An online speed profile generation approach for efficient airport ground movement

The precise guidance and control of taxiing aircraft based on four-dimensional trajectories (4DTs) has been recognised as a promising means to ensure safe and efficient airport ground movement in the context of ever growing air traffic demand. In this paper, a systematic approach for online speed profile generation is proposed. The aim is to generate fuel-efficient speed profiles respecting the timing constraints imposed by routing and scheduling, which ensures conflict-free movement of aircraft in the planning stage. The problem is first formulated as a nonlinear optimisation model, which uses a more flexible edge-based speed profile definition. A decomposed solution approach (following the framework of matheuristic) is then proposed to generate feasible speed profiles in real time. The decomposed solution approach reduces the nonlinear optimisation model into three tractable constituent problems. The control point arrival time allocation problem is solved using linear programming. The control point speed allocation problem is solved using particle swarm optimisation. And the complete speed profile between control points is determined using enumeration. Finally, improved speed profiles are generated through further optimisation upon the feasible speed profiles. The effectiveness and advantages of the proposed approach are validated using datasets of real-world airports.     

Crosswind‐based optimization of multiple runway orientations

The runway orientation must satisfy the operational requirements of aircraft for landing and takeoff. Actually, the runway orientation is the result of compromises between the airport usability (wind coverage) and additional factors, such as available land, existing obstructions, topographic difficulties, flight path interference among runways and airports, noise pollution, and other environmental impacts. Therefore, the solution of a combination of acceptable runway orientations, which avoids excessive crosswinds at least 95% of the time, as well as the optimal orientation solution, is essential to conduct those compromises in the runway orientation analysis. The objective of this paper is to develop a computer model, named the optimization of multiple runway orientations model, which is capable of simultaneously providing a combination of acceptable runway orientations, changing the allowable crosswind limit flexibly, and determining the optimal orientations of multiple runway configurations. Instead of visual estimation or geometric computation, this paper presents an analytical method for wind coverage analysis. The model is mainly running in spreadsheet and Visual Basic for Applications (VBA). The numerical example and comparison show that the optimization of multiple runway orientations model is competitively accurate and convenient in comparison with previous ones. This paper presents an up‐to‐date model for the optimization of multiple runway orientations. By combining it with the geographic information system obstructions model, it can become an essential element of a future model for airport development cost minimization that combines airfield land use, earthwork volume, and cost estimation modules. Copyright © 2013 John Wiley & Sons, Ltd.     

Demonstration of reduced airport congestion through pushback rate control

Airport surface congestion results in significant increases in taxi times, fuel burn and emissions at major airports. This paper describes the field tests of a congestion control strategy at Boston Logan International Airport. The approach determines a suggested rate to meter pushbacks from the gate, in order to prevent the airport surface from entering congested states and to reduce the time that flights spend with engines on while taxiing to the runway. The field trials demonstrated that significant benefits were achievable through such a strategy: during eight four-hour tests conducted during August and September 2010, fuel use was reduced by an estimated 12,250–14,500 kg (4000–4700 US gallons), while aircraft gate pushback times were increased by an average of only 4.4 min for the 247 flights that were held at the gate.     

Real-time airport surface movement planning: Minimizing aircraft emissions

This paper presents a study towards the development of a real-time taxi movement planning system that seeks to optimize the timed taxiing routes of all aircraft on an airport surface, by minimizing the emissions that result from taxiing aircraft operations. To resolve this online planning problem, one of the most commonly employed operations research methods for large-scale problems has been successfully used, viz., mixed-integer linear programming (MILP). The MILP formulation implemented herein permits the planning system to update the total taxi planning every 15 s, allowing to respond to unforeseen disturbances in the traffic flow. Extensive numerical experiments involving a realistic (hub) airport environment bear out that an estimated environmental benefit of 1–3 percent per emission product can be obtained. This research effort clearly demonstrates that a surface movement planning system capable of minimizing the emissions in conjunction with the total taxiing time can be beneficial for airports that face dense surface traffic and stringent environmental requirements.     

Integrated sequencing and merging aircraft to parallel runways with automated conflict resolution and advanced avionics capabilities

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.     

Metaheuristics for efficient aircraft scheduling and re-routing at busy terminal control areas

Intelligent decision support systems for the real-time management of landing and take-off operations can be very effective in helping air traffic controllers to limit airport congestion at busy terminal control areas. The key optimization problem to be solved regards the assignment of airport resources to take-off and landing aircraft and the aircraft sequencing on them. The problem can be formulated as a mixed integer linear program. However, since this problem is strongly NP-hard, heuristic algorithms are typically adopted in practice to compute good quality solutions in a short computation time. This paper presents a number of algorithmic improvements implemented in the AGLIBRARY solver (a state-of-the-art optimization solver to deal with complex routing and scheduling problems) in order to improve the possibility of finding good quality solutions quickly. The proposed framework starts from a good initial solution for the aircraft scheduling problem with fixed routes (given the resources to be traversed by each aircraft), computed via a truncated branch-and-bound algorithm. A metaheuristic is then applied to improve the solution by re-routing some aircraft in the terminal control area. New metaheuristics, based on variable neighbourhood search, tabu search and hybrid schemes, are introduced. Computational experiments are performed on an Italian terminal control area under various types of disturbances, including multiple aircraft delays and a temporarily disrupted runway. The metaheuristics achieve solutions of remarkable quality, within a small computation time, compared with a commercial solver and with the previous versions of AGLIBRARY.     

Preference-based evolutionary algorithm for airport surface operations

In addition to time efficiency, minimisation of fuel consumption and related emissions has started to be considered by research on optimisation of airport surface operations as more airports face severe congestion and tightening environmental regulations. Objectives are related to economic cost which can be used as preferences to search for a region of cost efficient and Pareto optimal solutions. A multi-objective evolutionary optimisation framework with preferences is proposed in this paper to solve a complex optimisation problem integrating runway scheduling and airport ground movement problem. The evolutionary search algorithm uses modified crowding distance in the replacement procedure to take into account cost of delay and fuel price. Furthermore, uncertainty inherent in prices is reflected by expressing preferences as an interval. Preference information is used to control the extent of region of interest, which has a beneficial effect on algorithm performance. As a result, the search algorithm can achieve faster convergence and potentially better solutions. A filtering procedure is further proposed to select an evenly distributed subset of Pareto optimal solutions in order to reduce its size and help the decision maker. The computational results with data from major international hub airports show the efficiency of the proposed approach.     

Modeling effects of different air traffic control operational procedures,separation rules,and service disciplines on runway landing capacity

This paper deals with modeling the possible effects of different advanced procedures, existing, innovative, and new air traffic control (ATC) separation rules, and service disciplines on the ultimate landing capacity of a single runway. The first implies a combination and/or exclusive use of conventional and steeper final approach and landing procedures. The second includes the current horizontal, innovative mixed horizontal/vertical and new vertical distance‐based and time‐based separation rules. The last embrace the common First Come, First Served and innovative Priority service discipline. Such increasingly complex and challenging applications are assumed to be based on the new technologies on‐board the aircraft and at the ATC to be developed in the scope of the current United States Next Generation Air Transport System and European Single European Sky ATM Research programs. The convenient analytical models for calculating the runway landing capacity are developed and applied to the generic case of a single runway according to the “what–if” scenario approach. This enables carrying out the sensitivity analysis of the landing capacity with respect to the most influential factors – the ATC advanced operational procedures, separation rules, service disciplines, and aircraft fleet mix. Copyright © 2013 John Wiley & Sons, Ltd.     

Implementation of a thrust reverse noise detection system for airports

A thrust reverse noise detection methodology for airports is developed and tested in terms of meeting the restrictions at Madrid-Barajas airport. The detection is based on thresholds applied to the sound power level time histories estimated using a microphone array and an inverse sound propagation model. The two sound events detected are classified as landing and thrust reverse to complete the identification of thrust reverse activation. The methodology performs well especially in the detection stage because the estimation of sound power level enhances the sound events and their separation, even if landing and thrust reverse are close to each other. The implementation of the TREND tool has error rates lower than 10%.     

Evaluating the operational performance of airside and landside at Chinese airports with novel inputs

ABSTRACT

This paper evaluates the operational performance of airside and landside at Chinese airports with two novel inputs. Furthermore, the airport landside operation is decomposed into passenger-terminal operations and cargo-warehouse operations. One novel input is the capability of the runway system which is introduced into airside performance evaluation. The other novel input is cargo warehousing which is introduced into landside performance evaluation. To address multiple optimal solutions when estimating Returns to Scale in Data Envelopment Analysis, we adopted the Zhu and Shen method. This empirical study shows that neither the number of runways nor total runway length is a genuine index of runway system capability in the evaluation of airside performance. Only four airports achieved full efficiency in all eight measures while eight airports did not achieve any full efficiency. In addition, one airport did not perform well in the benchmark analysis.     

A multi-criteria evaluation of solutions and alternatives for matching capacity to demand in an airport system: the case of London

This paper applies multi-criteria decision-making (MCDM) methods to the evaluation of solutions and alternatives for matching airport system airside (runway) capacity to demand. For such a purpose, ‘building a new runway’ is considered as the solution and candidate airports of the system as alternatives for implementing the solution. The alternative airports are characterized by their physical/spatial, operational, economic, environmental, and social performance represented by corresponding indicator systems which, after being defined and estimated under given operating scenarios, are used as evaluation attributes/criteria by the selected MCDM methods. Two MCDM methods – Simple Additive Weighting and Technique for Order of Preference by Similarity to Ideal Solution – are applied to the case of the London airport system to rank and select the preferred alternative from three candidate airports – Heathrow, Gatwick, and Stansted – for where a new runway could be built.     

Heuristic search for the coupled runway sequencing and taxiway routing problem

This paper presents the first local search heuristic for the coupled runway sequencing (arrival & departure) and taxiway routing problems, based on the receding horizon (RH) scheme that takes into account the dynamic nature of the problem. As test case, we use Manchester Airport, the third busiest airport in the UK. From the ground movement perspective, the airport layout requires that departing aircraft taxi across the arrivals runway. This makes it impossible to separate arrival from departure sequencing in practice. Operationally, interactions between aircraft on the taxiways could prevent aircraft from taking off from, or landing on, runways during the slots assigned to them by an algorithm optimizing runway use alone. We thus consider the interactions between arrival and departure aircraft on the airport surface. Compared to sequentially optimized solutions, the results obtained with our approach indicate a significant decrease in the taxiway routing delay, with generally no loss in performance in terms of the sequencing delay for a regular day of operations. Another benefit of such a simultaneous optimization approach is the possibility of holding aircraft at the stands for longer, without the engines running. This significantly reduces the fuel burn, as well as bottlenecks and traffic congestion during peak hours that are often the cause of flight delays due to the limited amount of airport surface space available. Given that the maximum computing time per horizon is around 95 s, real-time operation might be practical with increased computing power.     

Modelling the capacity of closely-spaced parallel runways using innovative approach procedures

This paper develops analytical models for calculating the ultimate arrival, departure, and mixed operation capacity of closely-spaced parallel runways. Each capacity is defined as the maximum number of corresponding aircraft operations accommodated during a given period of time (usually one or a quarter of an hour) under constant (i.e. sustained) demand for service. As combined, they enable the capacity coverage curve to be synthesized. In particular, the capacity model for arrivals assumes the use of two rather innovative approach procedures – the Staggered Approach Procedure (SGAP) and the Steeper Approach Procedure (SEAP) in combination with the baseline Conventional Approach Procedure (CNAP) under Instrument Meteorological Conditions (IMC) and Instrumental Flight Rules (IFR).The model for arrival capacity that aims to estimate potential of these procedures uses main inputs such as: the geometry of given parallel runways and innovative SGAP and SEAP, and baseline CNAP; the Air Traffic Control (ATC) minimum separation rules; aircraft fleet mix characterized by the wake-vortex categories; capability of using different ILS Glide (GS) angles; final approach speeds; and the arrival runway occupancy time. In addition, the model for departure capacity uses inputs that embrace: the ATC separation rules between successive departures; aircraft fleet mix; and corresponding runway occupancy times. Finally, the model for mixed operation capacity uses inputs such as: the ATC arrival–departure separation rules; corresponding runway occupancy times; aircraft fleet mix for each type of operations; and proportion of the arrival and departure demand.The models are applied to calculating the ultimate capacity of closely-spaced parallel runways at two large airports, one in Europe and other in the US, assuming safe use of innovative SGAP and SEAP in combination with CNAP under IMC. The output from the models consists of the corresponding capacities and their variations depending on particular inputs.     

An optimization model for aircraft maintenance scheduling and re-assignment

The aircraft maintenance scheduling is one among the major decisions an airline has to make during its operation. Though maintenance scheduling comes as an end stage in an airline operation, it has potential for cost savings. Maintenance scheduling is an easily understood but difficult to solve problem. Given a flight schedule with aircraft assigned to it, the aircraft maintenance-scheduling problem is to determine which aircraft should fly which segment and when and where each aircraft should undergo different levels of maintenance check required by the Federal Aviation Administration. The objective is to minimize the maintenance cost and any costs incurred during the re-assignment of aircraft to the flight segments.This paper provides a complete formulation for maintenance scheduling and a heuristic approach to solve the problem. The heuristic procedure provides good solutions in reasonable computation time. This model can be used by mid-sized airline corporations to optimize their maintenance costs.     

A generalized multiple durations proportional hazard model with an application to activity behavior during the evening work-to-home commute

The model developed in this paper generalizes (in the context of multiple exit states from a duration spell) extant competing risk methods which tie the exit state of duration very tightly with the length of duration. In the current formulation, the exit state is modeled explicitly and jointly with duration models for each potential exit state. The model developed here, however, is much broader in its applicability than only to the competing risk situation; it is applicable to multiple durations arising from multiple entrance states, multiple exit states, or a combination of entrance and exit states. Multiple entrance states occur frequently in many situations, but have received little attention in the literature. Explicit consideration of the entrance state is important, even in single or multiple competing risk models in order to accommodate the sample selection in duration based on the no-entry/entry (to the duration spell) outcome. The generalized multiple durations model developed in the paper is applied to an empirical analysis of activity behavior during the return home from work.     

Scheduling of airport runway operations using stochastic branch and bound methods

In this paper we present a solution methodology based on the stochastic branch and bound algorithm to find optimal, or close to optimal, solutions to the stochastic airport runway scheduling problem. The objective of the scheduling problem is to find a sequence of aircraft operations on one or several runways that minimizes the total makespan, given uncertain aircraft availability at the runway. Enhancements to the general stochastic branch and bound algorithm are proposed and we give the specific details pertaining to runway scheduling. We show how the algorithm can be terminated early with solutions that are close to optimal, and investigate the impact of the uncertainty level. The computational experiment indicates that the sequences obtained using the stochastic branch and bound algorithm have, on average, 5–7% shorter makespans than sequences obtained using deterministic sequencing models. In addition, the proposed algorithm is able to solve instances with 14 aircraft using less than 1 min of computation time.     

Determining the effects of scheduling on automated automobile guideway utilization

This paper develops an analytic approach for measuring the effect of vehicle scheduling and of metering methods required to balance entrance rates among stations on lane capacity utilization of automated automobile guideways. The scheduling process at each entrance is represented by a probabilistic model which generates a system of nonlinear equations. The solution of this system yields the maximum steady state input rates at the entrances to the guideway system. The method developed is applicable to network configurations in which a small number of merges must be scheduled for each vehicle before it enters the guideway. It is demonstrated by application to a corridor guideway serving a major activity center during peak inbound demand, and also it is shown how the approach may be extended to more complex networks.     

An integrated modelling approach for the bicriterion vehicle routing and scheduling problem with environmental considerations

The consideration of pollution in routing decisions gives rise to a new routing framework where measures of the environmental implications are traded off with business performance measures. To address this type of routing decisions, we formulate and solve a bi-objective time, load and path-dependent vehicle routing problem with time windows (BTL-VRPTW). The proposed formulation incorporates a travel time model representing realistically time varying traffic conditions. A key feature of the problem under consideration is the need to address simultaneously routing and path finding decisions. To cope with the computational burden arising from this property of the problem we propose a network reduction approach. Computational tests on the effect of the network reduction approach on determining non-dominated solutions are reported. A generic solution framework is proposed to address the BTL-VRPTW. The proposed framework combines any technique that creates capacity-feasible routes with a routing and scheduling method that aims to convert the identified routes to problem solutions. We show that transforming a set of routes to BTL-VRPTW solutions is equivalent to solving a bi-objective time dependent shortest path problem on a specially structured graph. We propose a backward label setting technique to solve the emerging problem that takes advantage of the special structure of the graph. The proposed generic solution framework is implemented by integrating the routing and scheduling method into an Ant Colony System algorithm. The accuracy of the proposed algorithm was assessed on the basis of its capability to determine minimum travel time and fuel consumption solutions. Although the computational results are encouraging, there is ample room for future research in algorithmic advances on addressing the proposed problem.