A fuzzy approach to addressing uncertainty in Airport Ground Movement optimisation

Funded by Engineering and Physical Sciences Research Counci

A Collaborative Optimization Model for Ground Taxi Based on Aircraft Priority

Large hub airports have gradually become the “bottleneck” of the air transport network. To alleviate the “bottleneck” effect, optimizing the taxi scheduling is one of the solutions. This paper establishes a scheduling optimization model by introducing priority of aircraft under collaborative decision-making mechanism, and a genetic algorithm is designed to verify the scheduling model by simulating. Optimization results show that the reliability of the model and the adjusted genetic algorithm have a high efficiency. The taxiing time decreases by 2.26% when compared with an empirical method and the flights with higher priorities are assigned better taxi routes. It has great significance in reducing flight delays and cost of operation

A chance-constrained programming model for airport ground movement optimisation with taxi time uncertainties

Airport ground movement remains a major bottleneck for air traffic management. Existing approaches have developed several routing allocation methods to address this problem, in which the taxi time traversing each segment of the taxiways is fixed. However, taxi time is typically difficult to estimate in advance, since its uncertainties are inherent in the airport ground movement optimisation due to various unmodelled and unpredictable factors. To address the optimisation of taxi time under uncertainty, we introduce a chance-constrained programming model with sample approximation, in which a set of scenarios is generated in accordance with taxi time distributions. A modified sequential quickest path searching algorithm with local heuristic is then designed to minimise the entire taxi time. Working with real-world data at an international airport, we compare our proposed method with the state-of-the-art algorithms. Extensive simulations indicate that our proposed method efficiently allocates routes with smaller taxiing time, as well as fewer aircraft stops during the taxiing process

Enhancing decision support systems for airport ground movement

With the expected continued increases in air transportation, the mitigation of the consequent delays and environmental effects is becoming more and more important, requiring increasingly sophisticated approaches for airside airport operations. The ground movement problem forms the link between other airside problems at an airport, such as arrival sequencing, departure sequencing, gate/stand allocation and stand holding. The purpose of this thesis is to contribute to airport ground movement research through obtaining a better understanding of the problem and producing new models and algorithms for three sub-problems. Firstly, many stakeholders at an airport can benefit from more accurate taxi time predictions. This thesis focuses upon this aim by analysing the important factors affecting taxi times for arrivals and departures and by comparing different regression models to analyse which one performs the best for this particular task. It was found that incorporating the information of the airport layout could significantly improve the accuracy and that a TSK fuzzy rule-based system outperformed other approaches. Secondly, a fast and flexible decision support system is introduced which can help ground controllers in an airport tower to make better routing and scheduling decisions and can also absorb as much of the waiting time as possible for departures at the gate/stand, to reduce the fuel burn and environmental impact. The results show potential maximum savings in total taxi time of about 30.3%, compared to the actual performance at the airport. Thirdly, a new research direction is explored which analyses the trade-off between taxi time and fuel consumption during taxiing. A sophisticated new model is presented to make such an analysis possible. Furthermore, this research provides the basis for integrating the ground movement problem with other airport operations. Datasets from Zurich Airport, Stockholm-Arlanda Airport, London Heathrow Airport and Hartsfield-Jackson Atlanta International Airport were utilised to test these sub-problems

Enhancing decision support systems for airport ground movement

With the expected continued increases in air transportation, the mitigation of the consequent delays and environmental effects is becoming more and more important, requiring increasingly sophisticated approaches for airside airport operations. The ground movement problem forms the link between other airside problems at an airport, such as arrival sequencing, departure sequencing, gate/stand allocation and stand holding. The purpose of this thesis is to contribute to airport ground movement research through obtaining a better understanding of the problem and producing new models and algorithms for three sub-problems. Firstly, many stakeholders at an airport can benefit from more accurate taxi time predictions. This thesis focuses upon this aim by analysing the important factors affecting taxi times for arrivals and departures and by comparing different regression models to analyse which one performs the best for this particular task. It was found that incorporating the information of the airport layout could significantly improve the accuracy and that a TSK fuzzy rule-based system outperformed other approaches. Secondly, a fast and flexible decision support system is introduced which can help ground controllers in an airport tower to make better routing and scheduling decisions and can also absorb as much of the waiting time as possible for departures at the gate/stand, to reduce the fuel burn and environmental impact. The results show potential maximum savings in total taxi time of about 30.3%, compared to the actual performance at the airport. Thirdly, a new research direction is explored which analyses the trade-off between taxi time and fuel consumption during taxiing. A sophisticated new model is presented to make such an analysis possible. Furthermore, this research provides the basis for integrating the ground movement problem with other airport operations. Datasets from Zurich Airport, Stockholm-Arlanda Airport, London Heathrow Airport and Hartsfield-Jackson Atlanta International Airport were utilised to test these sub-problems

Integrated and joint optimisation of runway-taxiway-apron operations on airport surface

Airports are the main bottlenecks in the Air Traffic Management (ATM) system. The predicted 84% increase in global air traffic in the next two decades has rendered the improvement of airport operational efficiency a key issue in ATM. Although the operations on runways, taxiways, and aprons are highly interconnected and interdependent, the current practice is not integrated and piecemeal, and overly relies on the experience of air traffic controllers and stand allocators to manage operations, which has resulted in sub-optimal performance of the airport surface in terms of operational efficiency, capacity, and safety. This thesis proposes a mixed qualitative-quantitative methodology for integrated and joint optimisation of runways, taxiways, and aprons, aiming to improve the efficiency of airport surface operations by integrating the operations of all three resources and optimising their coordination. This is achieved through a two-stage optimisation procedure: (1) the Integrated Apron and Runway Assignment (IARA) model, which optimises the apron and runway allocations for individual aircraft on a pre-tactical level, and (2) the Integrated Dynamic Routing and Off-block (IDRO) model, which generates taxiing routes and off-block timing decisions for aircraft on an operational (real-time) level. This two-stage procedure considers the interdependencies of the operations of different airport resources, detailed network configurations, air traffic flow characteristics, and operational rules and constraints. The proposed framework is implemented and assessed in a case study at Beijing Capital International Airport. Compared to the current operations, the proposed apron-runway assignment reduces total taxiing distance, average taxiing time, taxiing conflicts, runway queuing time and fuel consumption respectively by 15.5%, 15.28%, 45.1%, [58.7%, 35.3%, 16%] (RWY01, RWY36R, RWY36L) and 6.6%; gated assignment is increased by 11.8%. The operational feasibility of this proposed framework is further validated qualitatively by subject matter experts (SMEs). The potential impact of the integrated apron-runway-taxiway operation is explored with a discussion of its real-world implementation issues and recommendations for industrial and academic practice.Open Acces

Methods And Sources For Underpinning Airport Ground Movement Decision Support Systems

The airport Ground Movement problem is concerned with the allo- cation of routes to aircraft for their travel along taxiways between the runway and the stands. It is important to find high quality solutions to this problem because it has a strong influence on the capacity of an airport and upon the environmental impact. The problem is particularly challenging. It has multiple objectives (such as minimising taxi time and fuel consumption). It also has considerable uncertainty, which arises from the complex operations of an airport. It is an active and topical area of research. A barrier to scientific research in this area is the lack of publicly available realistic data and benchmark problems. The reason for this is often concerned with commercial sensitivities. We have worked with airports and service providers to address this issue, by exploring several sources of freely-available data and developing algorithms for cleaning and processing the data into a more suitable form. The result is a system to generate datasets that are realistic, and that facilitate research with the potential to improve on real-world problems, without the confidentiality and commercial licensing issues usually associated with real airport data. Case studies with several international airports demonstrate the usefulness of the datasets. The algorithms have been implemented within three tools and made freely-available for researchers. A benchmark Ground Movement problem has also been made available, with results for an existing Ground Movement algorithm. It is intended that these contributions will underpin the advance of research in this difficult application area

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

Towards a more realistic, cost effective and greener ground movement through active routing: part 1 - optimal speed profile generation

Among all airport operations, aircraft ground movement plays a key role in improving overall airport capacity as it links other airport operations. Moreover, ever increasing air traffic, rising costs and tighter environmental targets create a pressure to minimise fuel burn on the ground. However, current routing functions envisioned in Advanced Surface Movement, Guidance and Control Systems (A-SMGCS) almost exclusively consider the most time efficient solution and apply a conservative separation to ensure conflict free surface movement, sometimes with additional buffer times to absorb small deviations from the taxi times. Such an overly constrained routing approach may result in either a too tight planning for some aircraft so that fuel efficiency is compromised due to multiple acceleration phases, or performance could be further improved by reducing the separation and buffer times. In light of this, Part 1 and 2 of this paper present a new Active Routing framework with the aim of providing a more realistic, cost effective and environmental friendly surface movement, targeting some of the busiest international hub airports. Part 1 of this paper focuses on optimal speed profile generation using a physics based aircraft movement model. Two approaches based respectively on the Base of Aircraft Data (BADA) and the International Civil Aviation Organization (ICAO) engine emissions database have been employed to model fuel consumption. These models are then embedded within a mutli-objective optimization framework to capture the essence of different speed profiles in a Pareto optimal sense. The proposed approach represents the first attempt to systematically address speed profiles with competing objectives. Results reveal an apparent trade-off between fuel burn and taxi times irrespective of fuel consumption modelling approaches. This will have a profound impact on the routing and scheduling, and open the door for the new concept of Active Routing discussed in Part 2 of this paper