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

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

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