Lessons from building an automated pre-departure sequencer for airports

Commercial airports are under increasing pressure to comply with the Eurocontrol collaborative decision making (CDM) initiative, to ensure that information is passed between stakeholders, integrate automated decision support or make predictions. These systems can also aid effective operations beyond the airport by communicating scheduling decisions to other relevant parties, such as Eurocontrol, for passing on to downstream airports and enabling overall airspace improvements. One of the major CDM components is aimed at producing the target take-off times and target startup-approval times, i.e. scheduling when the aircraft should push back from the gates and start their engines and when they will take off. For medium-sized airports, a common choice for this is a “pre-departure sequencer” (PDS). In this paper, we describe the design and requirements challenges which arose during our development of a PDS system for medium sized international airports. Firstly, the scheduling problem is highly dynamic and event driven. Secondly, it is important to end-users that the system be predictable and, as far as possible, transparent in its operation, with decisions that can be explained. Thirdly, users can override decisions, and this information has to be taken into account. Finally, it is important that the system is as fair as possible for all users of the airport, and the interpretation of this is considered here. Together, these factors have influenced the design of the PDS system which has been built to work within an existing large system which is being used at many airport

Human performance and strategies while solving an aircraft routing and sequencing problem: an experimental approach

As airport resources are stretched to meet increasing demand for services, effective use of ground infrastructure is increasingly critical for ensuring operational efficiency. Work in operations research has produced algorithms providing airport tower controllers with guidance on optimal timings and sequences for flight arrivals, departures, and ground movement. While such decision support systems have the potential to improve operational efficiency, they may also affect users’ mental workload, situation awareness, and task performance. This work sought to identify performance outcomes and strategies employed by human decision makers during an experimental airport ground movement control task with the goal of identifying opportunities for enhancing user-centered tower control decision support systems. To address this challenge, thirty novice participants solved a set of vehicle routing problems presented in the format of a game representing the airport ground movement task practiced by runway controllers. The games varied across two independent variables, network map layout (representing task complexity) and gameplay objective (representing task flexibility), and verbal protocol, visual protocol, task performance, workload, and task duration were collected as dependent variables. A logistic regression analysis revealed that gameplay objective and task duration significantly affected the likelihood of a participant identifying the optimal solution to a game, with the likelihood of an optimal solution increasing with longer task duration and in the less flexible objective condition. In addition, workload appeared unaffected by either independent variable, but verbal protocols and visual observations indicated that high-performing participants demonstrated a greater degree of planning and situation awareness. Through identifying human behavior during optimization problem solving, the work of tower control can be better understood, which, in turn, provides insights for developing decision support systems for ground movement management

The effects of pushback delays on airport ground movement

With the constant increase in air traffic, airports are facing capacity problems. Optimisation methods for specific airport processes are starting to be increasingly utilised by many large airports. However, many processes do happen in parallel, and maximising the potential benefits will require a more complex optimisation model, which can consider multiple processes simultaneously and take into account the detailed complexities of the processes where necessary, rather than using more abstract models. This paper focuses on one of these complexities, which is usually ignored in ground movement planning; showing the importance of the pushback process in the routing process. It investigates whether taking the pushback process into consideration can result in the prediction of delays that would otherwise pass unnoticed. Having an accurate model for the pushback process is important for this and identifying all of the delays that may occur can lead to more accurate and realistic models that can then be used in the decision making process for ground movement operations. After testing two different routing methods with a more detailed pushback process, we found that many of the delays are not predicted if the pushback process is not explicitly modelled. Having a more precise model, with accurate movements of aircraft is very important for any integrated model and will allow ground movement models to be of use in more reliable integrated decision making systems at airports. Minimising these delays can help airports increase their capacity and become more environmentally friendly

The effects of pushback delays on airport ground movement

With the constant increase in air traffic, airports are facing capacity problems. Optimisation methods for specific airport processes are starting to be increasingly utilised by many large airports. However, many processes do happen in parallel, and maximising the potential benefits will require a more complex optimisation model, which can consider multiple processes simultaneously and take into account the detailed complexities of the processes where necessary, rather than using more abstract models. This paper focuses on one of these complexities, which is usually ignored in ground movement planning; showing the importance of the pushback process in the routing process. It investigates whether taking the pushback process into consideration can result in the prediction of delays that would otherwise pass unnoticed. Having an accurate model for the pushback process is important for this and identifying all of the delays that may occur can lead to more accurate and realistic models that can then be used in the decision making process for ground movement operations. After testing two different routing methods with a more detailed pushback process, we found that many of the delays are not predicted if the pushback process is not explicitly modelled. Having a more precise model, with accurate movements of aircraft is very important for any integrated model and will allow ground movement models to be of use in more reliable integrated decision making systems at airports. Minimising these delays can help airports increase their capacity and become more environmentally friendly

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

On-line decision support for take-off runaway scheduling at London Heathrow Airport

The research problem considered in this thesis was presented by NATS, who are responsible for the take-off runway scheduling at London Heathrow airport. The sequence in which aircraft take off is very important and can have a huge effect upon the throughput of the runway and the consequent delay for aircraft awaiting take-off. Sequence-dependent separations apply between aircraft at take-off, some aircraft have time-slots within which they must take-off and all re-sequencing performed by the runway controller has to take place within restrictive areas of the airport surface called holding areas. Despite the complexity of the task and the short decision time available, take-off sequencing is performed manually by runway controllers. In such a rapidly changing environment, with much communication and observation demanded of the busy controller, it is hardly surprising that sub-optimal mental heuristics are currently used. The task presented by NATS was to develop the decision-making algorithms for a decision support tool to aid a runway controller to solve this complex real-world problem. A design for such a system is presented in this thesis. Although the decision support system presents only a take-off sequence to controllers, it is vitally important that the movement within the holding area that is required in order to achieve the re-sequencing is both easy to identify and acceptable to controllers. A key objective of the selected design is to ensure that this will always be the case. Both regulatory information and details of controller working methods and preferences were utilised to ensure that the presented sequences will not only be achievable but will also be acceptable to controllers. A simulation was developed to test the system and permit an evaluation of the potential benefits. Experiments showed that the decision support system found take-off sequences which significantly reduced the delay compared with those that the runway controllers actually used. These sequences had an equity of delay comparable with that in the sequences the controllers generated, and were achieved in a very similar way. Much of the benefit that was gained was a result of the decision support system having visibility of the taxiing aircraft in addition to those already queueing for the runway. The effects of uncertainty in taxi times and differing planning horizons are explicitly considered in this thesis. The limited decision time available ensures that it is not practical for a runway controller to consider as many aircraft as the decision support algorithms can. The results presented in this thesis indicate that huge benefits may be possible from the development of a system to simplify the sequencing task for the controllers while simultaneously giving them greater visibility of taxiing aircraft. Even beyond these benefits, however, the system described here will also be seen to have further potential benefits, such as for evaluating the effects of constraints upon the departure system or the flexibility of holding area structures

A fuzzy approach to addressing uncertainty in Airport Ground Movement optimisation

Funded by Engineering and Physical Sciences Research Counci

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

On-line decision support for take-off runaway scheduling at London Heathrow Airport

The research problem considered in this thesis was presented by NATS, who are responsible for the take-off runway scheduling at London Heathrow airport. The sequence in which aircraft take off is very important and can have a huge effect upon the throughput of the runway and the consequent delay for aircraft awaiting take-off. Sequence-dependent separations apply between aircraft at take-off, some aircraft have time-slots within which they must take-off and all re-sequencing performed by the runway controller has to take place within restrictive areas of the airport surface called holding areas. Despite the complexity of the task and the short decision time available, take-off sequencing is performed manually by runway controllers. In such a rapidly changing environment, with much communication and observation demanded of the busy controller, it is hardly surprising that sub-optimal mental heuristics are currently used. The task presented by NATS was to develop the decision-making algorithms for a decision support tool to aid a runway controller to solve this complex real-world problem. A design for such a system is presented in this thesis. Although the decision support system presents only a take-off sequence to controllers, it is vitally important that the movement within the holding area that is required in order to achieve the re-sequencing is both easy to identify and acceptable to controllers. A key objective of the selected design is to ensure that this will always be the case. Both regulatory information and details of controller working methods and preferences were utilised to ensure that the presented sequences will not only be achievable but will also be acceptable to controllers. A simulation was developed to test the system and permit an evaluation of the potential benefits. Experiments showed that the decision support system found take-off sequences which significantly reduced the delay compared with those that the runway controllers actually used. These sequences had an equity of delay comparable with that in the sequences the controllers generated, and were achieved in a very similar way. Much of the benefit that was gained was a result of the decision support system having visibility of the taxiing aircraft in addition to those already queueing for the runway. The effects of uncertainty in taxi times and differing planning horizons are explicitly considered in this thesis. The limited decision time available ensures that it is not practical for a runway controller to consider as many aircraft as the decision support algorithms can. The results presented in this thesis indicate that huge benefits may be possible from the development of a system to simplify the sequencing task for the controllers while simultaneously giving them greater visibility of taxiing aircraft. Even beyond these benefits, however, the system described here will also be seen to have further potential benefits, such as for evaluating the effects of constraints upon the departure system or the flexibility of holding area structures

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