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Airport turnaround is an important operational process that directly affects flight punctuality, airport capacity, and ground-handling efficiency. However, many turnaround activities are still monitored manually or through fragmented operational records, which can limit real-time visibility and delay identification. This study proposes a computer vision-based airport turnaround monitoring pipeline that integrates YOLOv11 object detection, Norfair multi-object tracking, and frame differencing-based motion analysis to extract key operational events from airport video footage. Publicly available turnaround footage from Shinshu Matsumoto Airport, Japan, was collected under different environmental conditions, including daytime, nighttime, rainy, after-rain, and transition lighting conditions. From selected videos, 1446 images were labeled into 11 airport turnaround object classes, including tow tug, aerobridge, airplane, baggage container, belt loader, belt loader roof, fuel line, fuel tanker, fuel tube, tractor, and window. The dataset was divided into training, validation, and testing sets using a 70:20:10 ratio. The trained YOLOv11 model achieved strong detection performance, with overall test an precision of 0.9609, recall of 0.9445, and mAP50 of 0.9617. To support activity-level interpretation beyond object detection, the proposed pipeline applies frame differencing within specific regions of interest, including the aerobridge window region for passenger deboarding and boarding detection, and the belt loader roof region for baggage unloading and loading detection. The extracted object detections, motion spikes, and temporal logs are then converted into a Gantt chart that summarizes major turnaround activities, including airplane parking, deboarding, baggage unloading, refueling, baggage loading, boarding, and pushback. The results demonstrate that the proposed modified YOLO-based pipeline can transform ordinary airport video footage into structured operational timelines, supporting more transparent, data-driven, and automated monitoring of airport turnaround processes.

Aviation is currently facing one of its greatest challenges: reconciling growing traffic demand with the need to drastically reduce climate-altering emissions. Hydrogen has emerged as one of the most promising alternatives to decarbonize air transport. However, it poses significant challenges related to cryogenic storage, safety, and the adaptation of the airport infrastructure. Aprons represent a critical issue, as the increased volume of fuel tanks and different refueling protocols directly impact airport operational capacity. This research fits within this framework by analyzing a Code 4E Italian airport over three time horizons: 2025, with an all-kerosene fleet; 2035, with a 25% penetration of hydrogen-powered class A and B aircraft; and 2045, with a further increase in the hydrogen share (75% class A and B and 15% class C). The study evaluates apron capacity using fast-time simulation and compares the outcomes with an analytical model. The results show good consistency between theoretical and simulated capacity. The 2035 and 2045 scenarios with the introduction of hydrogen-powered aircraft show a reduction in apron capacity between 16% and 5% compared to conventional scenarios.

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.

The imperative of the Active Turnaround, as a preventive management approach, is to ensure the company's health through business continuity, growth, and a good resilience capacity. Disruptive events are omnipresent, having unpredictable impacts on the organization's stability. These events can critically impact the companies' strategy, necessitating dramatic change. Under these imperatives, a successful Active Turnaround process is based on an exhaustive and accurate diagnosis process, which is the key prerequisite for understanding the company's health condition. An Active Turnaround process is similar to a medical act, addressed not to human beings, but to companies. Financial measures and other key performance indicators represent the effects, the symptoms, of the company's distress. Following this analogy, the main purpose of the turnaround process is to keep the body (in our case, the company) alive and well, and even more, to provide a sustainable good health condition. To achieve this in our technological eve, developing and implementing digital tools is a must. The paper details the turnaround diagnosis process applied to a Romanian transportation company, using an innovative customized software tool entitled RAISA (Remote Automated Interactive Self–Assessment) Digital Platform, which provides a structured image (Diagnosis snapshot-like) regarding the Company's health condition.

Boarding time constitutes a critical element of turnaround time, which is used to measure the efficiency of airline operations. Therefore, to reduce boarding time, it is imperative to reconsider traditional passenger boarding strategies to make them more efficient. In this sense, this study seeks to analyze the impact of different strategies on boarding times using discrete event simulation on an Airbus 320. Seven boarding strategies have been identified and considered in our study, as follows: random, back-to-front, outside-in, reverse pyramid, blocks, Steffen, and modified optimal. The impact of carrying hand luggage and the presence of priority passengers has been considered, as well as the impact of having a continuous arrival of passengers during the boarding process versus having all passengers available at boarding time. In general, simulation results have pointed out that the outside-in and reverse pyramid strategies are the most effective, improving boarding time by up to 15%, when compared to the random strategy. Moreover, the back-to-front strategy, which is generally implemented by airline companies, has been shown to be the most inefficient strategy. Efficient boarding strategies are expected to contribute to the sustainability of air travel by minimizing the turnaround time, improving operational efficiency, and reducing emissions.

The emergence of low-cost carriers (LCCs) has been a key catalyst for the development of the aviation industry in the last decade. Indeed, extensive research has been undertaken to analyze the business model and impact on the aviation sector and beyond. Despite recent developments in the LCC markets in Asia and Latin America, much of the research has been focused on developed countries. Therefore, the purpose of this book is to identify the premises and prerequisites of the LCC model, and assess whether this business model could be successful in other less-developed countries, in particular the countries of Sub-Saharan Africa. This book identifies various definitions that have been applied to describe the LCC business model. In essence the majority of researchers define LCCs as carriers which, through a variety of operational processes, have achieved a cost advantage over full-service carriers (FSCs). This cost advantage is, in most cases, translated to the consumers by a lower fare offering. Although many carriers are defined as LCCs, the LCC model has developed into many different variations since the original Southwest Airlines model, the first U.S. LCC, which began operations in the 1960s.

The automatic collection of key milestone nodes in the process of aircraft turnaround plays an important role in the development needs of airport collaborative decision-making. This article exploits a computer vision-based framework to automatically recognize activities of the flight in/off-block and docking/undocking and record corresponding key milestone nodes. The proposed framework, which seamlessly integrates state-of-the-art algorithms and techniques in the field of computer vision, comprises two modules for the preprocessing and collection of key milestones. The preprocessing module extracts the spatiotemporal information of the executor of key milestone nodes from the complex background of the airport ground. In the second module, aiming at two categories of key milestone nodes, namely, single-target-based nodes represented by in-block and off-block and interaction-of-two-targets-based nodes represented by docking and undocking stairs, two methods for the collection of key milestone are designed, respectively. Two datasets are constructed for the training, testing, and evaluation of the proposed framework. Results of field experiments demonstrate how the proposed framework can contribute to the automatic collection of these key milestone nodes by replacing the manual recording method routinely used today.

We provide an overview about the research done in the field of airport and airline operations with a specific focus on a fast, reliable and sustainable passenger boarding. The reliable prediction operational processes along the aircraft air-ground trajectory demands a comprehensive consideration of economic, environmental, and handling constraints of airlines and airports. In particular, the critical process of passenger boarding is driven by passengers’ ability to follow the proposed boarding procedures and is not controlled by operational experts. In this paper we implement and compare two individual-based approaches which cover both specific passenger behavior during boarding and operational airline constraints. Both models used similar input values, but exhibit different magnitudes in the benefit evaluation. Furthermore, we demonstrate that there are still unused potentials to further improve boarding progress by using innovative infrastructural adaptations inside the aircraft cabin.

Airline turnaround time is defined as the time required to unload an airplane after its arrival at the gate and to prepare it for departure again. In today’s era of exorbitant airport charges and rising fuel charges levied across the world, it becomes important for airline companies to come out with ways to cut back cost. Since there is very little control over fuel costs, one of the ways airline companies can cut back cost without compromising quality is by reducing Turnaround Time. In order to cut costs and increase performance, airlines work constantly on reducing turnaround time as these smaller and larger adaptations can be seen as important process innovations, and they are crucial for competitiveness. In the absence of such measures, some companies find themselves in situations wherein they end up with very low profits or very high losses. Airline Turnaround Time has hence become a very important and key parameter in determining the profitability of an airline company. Such situations can lead to a lot of undesirable consequences, as it has a direct impact on the survival of the organization due to a series of payment crisis one after another. This case study tries to explain the application of process of ongoing improvement, the basic approach of Theory of Constraint to improve turnaround time.

Purpose - Environmental uncertainty threatens many companies. This paper seeks to offer a strategic model to help imperiled firms overcome employee inertia, counter competitive forces, and speed organizational renewal.Design methodology approach - The paper presents a change model which outlines five major steps, each comprised of three elements. Examples from the field illustrate sequential stages of the cycle.Findings - This framework aligns human resources and organizational processes. By adhering to this approach, leaders can become transformational change agents.Practical implications - The proposed strategic format evolved from face-to-face discussions with exemplary turnaround leaders. In their visits to the author's classroom over the last six years, they offered insights that should prove beneficial to other leaders in turnaround situations.Originality value - The paper extends existing business models by providing a comprehensive set of action steps to engage all organizational members.