A metric for the real-time evaluation of the aircraft boarding progress

Future 4D aircraft trajectories demand comprehensive consideration of environmental, economic, and operational constraints, as well as reliable prediction of all aircraft-related processes. Mutual interdependencies between airports result in system-wide, far-reaching effects in the air traffic network (reactionary delays). To comply with airline/airport challenges over the day of operations, a change to an air-to-air perspective is necessary, with a specific focus on the aircraft ground operations as major driver for airline punctuality. Aircraft ground trajectories primarily consists of handling processes at the stand (deboarding, catering, fueling, cleaning, boarding, unloading, loading), which are defined as the aircraft turnaround. Turnaround processes are mainly controlled by ground handling, airport, or airline staff, except the aircraft boarding, which is driven by passengers’ experience and willingness/ability to follow the proposed boarding procedures. This paper provides an overview of the research done in the field of aircraft boarding and introduces a reliable, calibrated, and stochastic aircraft boarding model. The stochastic boarding model is implemented in a simulation environment to evaluate specific boarding scenarios using different boarding strategies and innovative technologies. Furthermore, the potential of a connected aircraft cabin as sensor network is emphasized, which could provide information on the current and future status of the boarding process.

Reliable and predictable ground operations are essential for 4D aircraft trajectories. Uncertainties in the airborne phase have significantly less impact on flight punctuality than deviations in aircraft ground operations. The ground trajectory of an aircraft primarily consists of the handling processes at the stand, defined as the aircraft turnaround, which are mainly controlled by operational experts. Only the aircraft boarding, which is on the critical path of the turnaround, is driven by the passengers' experience and willingness or ability to follow the proposed procedures. We propose a machine learning approach predict the boarding time. A validated boarding simulation provides data input for a recurrent neural network approach (discrete time series of boarding progress). In particular we use a Long Short-Term Memory model to learn the characteristic passenger behaviors over time.

<div class="section abstract"><div class="htmlview paragraph">Passenger boarding is always part of the critical path of the aircraft turnaround: both efficient boarding and online prediction of the boarding progress are essential for a reliable turnaround progress. However, the boarding progress is mainly controlled by the passenger behavior. A fundamental scientific approach for aircraft boarding enables the consideration of individual passenger behaviors and operational constraints in order to develop a sustainable concept for enabling a prediction of the boarding progress. A reliable microscopic simulation approach is used to model the passenger behavior, where the individual movement is defined as a one-dimensional, stochastic, and time/space discrete transition process. The simulation covers a broad range of behaviors and boarding strategies as well as the integration of new technologies and procedures. Future cabin management systems will provide an enabling infrastructure to further improve the overall turnaround process and to allow for on-line prediction of specific handling processes. The paper provides a method to indicate the progress of the aircraft boarding. In this context, the aircraft seats are used as a sensor network with the capability to detect the status (free or occupied) of each seat. These individual seat statuses are used to derive an aggregated interference potential of the current seating condition with regards to the passenger seating process. The interference potential is a major indicator for the expected aircraft boarding time. In combination with an integrated airline/airport information management (e.g. sequence of boarding passengers) the boarding progress will be transformed from a black box to a transparent progress with the operator’s online ability to react to significant deviations from the planned progress.</div></div>

Implementation and application of a stochastic aircraft boarding model

Machine learning approach to predict aircraft boarding

Reliable and predictable ground operations are essential for 4D aircraft trajectories. Uncertainties in the airborne phase have significantly less impact on flight punctuality than deviations in aircraft ground operations. The ground trajectory of an aircraft primarily consists of the handling processes at the stand, defined as the aircraft turnaround, which are mainly controlled by operational experts. Only the aircraft boarding, which is on the critical path of the turnaround, is driven by the passengers' experience and willingness or ability to follow the proposed procedures. We propose a machine learning approach predict the boarding time. A validated boarding simulation provides data input for a recurrent neural network approach (discrete time series of boarding progress). In particular we use a Long Short-Term Memory model to learn the characteristic passenger behaviors over time.

In this paper we address the prediction of aircraft boarding using a machine learning approach. Reliable process predictions of aircraft turnaround are an important element to further increase the punctuality of airline operations. In this context, aircraft turnaround is mainly controlled by operational experts, but the critical aircraft boarding is driven by the passengers’ experience and willingness or ability to follow the proposed procedures. Thus, we used a developed complexity metric to evaluate the actual boarding progress and a machine learning approach to predict the final boarding time during running operations. A validated passenger boarding model is used to provide reliable aircraft status data, since no operational data are available today. These data are aggregated to a time-based complexity value and used as input for our recurrent neural network approach for predicting the boarding progress. In particular we use a Long Short-Term Memory model to learn the dynamical passenger behavior over time with regards to the given complexity metric.

Efficient aircraft turnaround operations at airports are vital to ensure overall air traffic network performance. After the outbreak of COVID-19, the traditional aircraft ground handling process has changed significantly due to new requirements put forward by the pandemic prevention and control policy. To better understand how COVID-19 has affected ground handling operations, a discrete-event simulation model of turnaround is established to analyze the change in the whole turnaround process before and after the pandemic. The critical path of turnaround operations was used to identify the significantly affected subprocesses to which airports should pay attention. For a case study on the two busiest airports in China, the aircraft turnaround time increased by about 18% after COVID-19. Cabin cleaning, catering, and passenger embarking were the main processes in causing this increase. By evaluating the impact mechanism of COVID-19 on turnaround operations, the study sheds light on strategic, tactical, and operational approaches for relevant authorities.

Dynamic change of aircraft seat condition for fast boarding

A review of aircraft turnaround operations and simulations

Background: In the dynamic world of commercial aviation, the efficient management of ground handling (GH) operations in aircraft turnarounds is an increasingly complex challenge, often perceived as operational chaos. Methods: This paper introduces the “Critical Minute Theorem” (CMT), a novel framework that integrates mathematical architecture principles into the optimization of GH processes. CMT identifies singular temporal thresholds, tk* at which small local disturbances generate nonlinear, system-wide disruptions. Results: By formulating the turnaround as a set of algebraic dependencies and nonlinear differential relations, the case studies demonstrate that delays are not random but structurally determined. The practical contribution of this study lies in showing that early recognition and intervention at these critical minutes significantly reduces propagated delays. Three case analyses are presented: (i) a fueling delay initially causing 9 min of disruption, reduced to 3.7 min after applying CMT-based reordering; (ii) baggage mismatch scenarios where CMT-guided list restructuring eliminates systemic deadlock; and (iii) PRM assistance delays mitigated by up to 12–15 min through anticipatory task reorganization. Conclusions: These results highlight that CMT enables predictive, non-technological control in turnaround operations, repositioning the human analyst as an architect of time capable of restoring structure where the system tends to collapse.

Aircraft boarding is a process mainly impacted by the boarding sequence, passenger behaviour and the amount of hand luggage. Whereas these aspects are already addressed in scientific research and operational improvements, the influence of infrastructural changes are only focused upon in the context of future aircraft design. The innovative Side-Slip Seat technology holds the potential for sustainably improving the boarding time by providing a wide aisle during the boarding progress. A comprehensive, validated simulation environment is used to analyse the benefits of this technology and an adapted boarding strategy is identified using evolutionary algorithms. Considering the operational reality of the air transportation domain (e.g. seat load), the individual passenger behaviour (e.g. conformance to procedures), and operational deviations (e.g. delay), the Side-Slip Seat could fasten aircraft boarding by both 20% shorter average boarding time and a more stable boarding progress (smaller standard deviation).

Aircraft boarding is a process mainly impacted by the boarding sequence, passenger behaviour and the amount of hand luggage. Whereas these aspects are already addressed in scientific research and operational improvements, the influence of infrastructural changes are only focused upon in the context of future aircraft design. The innovative Side-Slip Seat technology holds the potential for sustainably improving the boarding time by providing a wide aisle during the boarding progress. A comprehensive, validated simulation environment is used to analyse the benefits of this technology and an adapted boarding strategy is identified using evolutionary algorithms. Considering the operational reality of the air transportation domain (e.g. seat load), the individual passenger behaviour (e.g. conformance to procedures), and operational deviations (e.g. delay), the Side-Slip Seat could fasten aircraft boarding by both 20% shorter average boarding time and a more stable boarding progress (smaller standard deviation).

Aircraft turnaround and industrial actions: How ground handlers' strikes affect airport airside operational efficiency

Ground handling services constitute an important element of airline operations and significantly affect traffic stability and punctuality. In this article, the existing and potential impact of airline handling on air traffic volatility is reviewed from the point of view of airlines and ground operations. The issues of airline expectations towards ground handling agents (including handling rates, turnaround time, passenger services, and ramp services) are explored. In addition, the impact of an airline’s schedule and the volatility of its operations on the performance and operational requirements of handling agents is discussed, including actions required by handling agents in response to the above challenges. The mechanism of how the volatility of an airline’s schedule and its operations may impact the volatility of ground operations (directly and indirectly) is considered. The statistics of airline delays caused by ground operations are presented and discussed. The issue of the correctness of air traffic delays reporting by airlines is investigated.Furthermore, this article investigates internal factors of ground handling agents and their impact on air traffic volatility. The existing and potential considerations discussed include staff management issues (in particular, employee rotation resulting in staff shortages and service quality, including punctuality), resources management, the ground service support equipment (including new developments aiming at limiting ground safety incidents), and their impact on performance.