Analytical approach to solve the problem of aircraft passenger boarding during the coronavirus pandemic

Schultz, Michael; Soolaki, Majid

doi:10.1016/j.trc.2020.102931

Cited by 36 publications

(27 citation statements)

References 56 publications

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“…In addition to safety and security requirements, pandemic requirements could become a third permanent constraint, sustainably changing both ground operations and passenger handling. For example, specific changes have been made to standard operating procedures for aircraft handling at the airport, such as requiring passengers to maintain a minimum distance when disembarking [59] and boarding [60], or additionally disinfecting the aircraft cabin [61]. These significant changes will impact aircraft ground time and overall airport performance.…”

Section: Discussionmentioning

confidence: 99%

An Adaptive Framework for Optimization and Prediction of Air Traffic Management (Sub-)Systems with Machine Learning

Reitmann

Schultz

2022

Aerospace

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Evaluating the performance of complex systems, such as air traffic management (ATM), is a challenging task. When regarding aviation as a time-continuous system measured in value-discrete time series via performance indicators and certain metrics, it is important to use sufficiently targeted mathematical models within the analysis. A consistent identification of system dynamics at the evaluation level, without dealing with the actual physical events of the system, transforms the analysis of time series into a system identification process, which ensures control of an unknown (or only partially known) system. In this paper, the requirements for mathematical modeling are presented in the form of a step-by-step framework, which can be derived from the formal process model of ATM. The framework is applied to representative datasets based on former experiments and publications, for whose prediction of boarding times and classification of flight delays with machine learning (ML) the framework presented here was used. While the training process of neural networks was described in detail there, the paper shown here focuses on the control options and optimization possibilities based on the trained models. Overall, the discussed framework represents a strict guideline for addressing data and machine learning (ML)-based analysis and metaheuristic optimization in ATM.

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Section: Discussionmentioning

confidence: 99%

An Adaptive Framework for Optimization and Prediction of Air Traffic Management (Sub-)Systems with Machine Learning

Reitmann

Schultz

2022

Aerospace

Self Cite

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“…[48] analyzed the impact of COVID-19 on the transportation sector and confirmed via mobility indexes that the impacts are unprecedented in light of earlier disease outbreaks. There are various other studies describing and analyzing the impact on aviation in general, e.g., on passenger boarding [49], passenger disembarking [50], and aircraft turn-around management [51]. Except for the above-listed negative impacts on aviation as a system, there is a set of positive impacts; for instance, in terms of reduced aviation-induced greenhouse gas emission during COVID-19 [52][53][54].…”

Section: Literature Reviewmentioning

confidence: 99%

Travel Bubbles in Air Transportation: Myth or Reality?

2022

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Aviation has been hit hard by COVID-19, with passengers stranded in remote destinations, airlines filing for bankruptcy, and uncertain demand scenarios for the future. Travel bubbles are discussed as one possible solution, meaning countries which have successfully constrained the spread of COVID-19 gradually increase their mutual international flights, returning to a degree of normality. This study aims to answer the question of whether travel bubbles are indeed observable in flight data for the year 2020. We take the year 2019 as reference and then search for anomalies in countries’ flight bans and recoveries, which could possibly be explained by having successfully implemented a travel bubble. To the best of our knowledge, this study is the first to try to address the identification of COVID-19 travel bubbles in real data. Our methodology and findings lead to several important insights regarding policy making, problems associated with the concept of travel bubbles, and raise interesting avenues for future research.

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“…Hotle and Mumbower [23], show that air traffic has decreased by up to 95%. Passenger handling has changed significantly [24]. Passenger flow through the security checkpoint is currently insufficient to determine performance empirically.…”

Section: Study On the Improvement Of Security Checkpoint Operationsmentioning

confidence: 99%

Feasibility of Reducing Operator-to-Passenger Contact for Passenger Screening at the Airport with Respect to the Power Consumption of the System

Kierzkowski

Kisiel

2021

Energies

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So far, airport security screening has only been analysed in terms of efficiency, level of service, and protection against any acts of unlawful interference. Screening procedures have not yet addressed the need to limit operator-to-passenger contact. However, the pandemic situation (COVID-19) has shown that it is a factor that can be a key protection for the health of passengers and operators. The purpose of this paper was to analyse the feasibility of reducing contact between operators and passengers in the airport security screening system by process management with respect to the power consumption of the system. Experimental research was conducted on a real system. A computer simulation was applied to estimate system performance and power consumption. The paper identifies the important findings that expand upon previous knowledge. The results showed that there are two key factors: the experience of operators and proper system structure. These factors can significantly reduce the number of operator-to-passenger contacts and, in parallel, provide lower energy consumption of the system. The results obtained in this article showed that proper management improves the process by up to 37%. This approach expands the World Health Organization’s policy of prevention against COVID-19 and helps to ensure sustainable process management.

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Analytical approach to solve the problem of aircraft passenger boarding during the coronavirus pandemic

Cited by 36 publications

References 56 publications

An Adaptive Framework for Optimization and Prediction of Air Traffic Management (Sub-)Systems with Machine Learning

An Adaptive Framework for Optimization and Prediction of Air Traffic Management (Sub-)Systems with Machine Learning

Travel Bubbles in Air Transportation: Myth or Reality?

Feasibility of Reducing Operator-to-Passenger Contact for Passenger Screening at the Airport with Respect to the Power Consumption of the System

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