Advancements in Passenger Processes at Airports from Aircraft Perspective

Schultz, Michael; Schmidt, Michael

doi:10.3390/su10113877

Cited by 5 publications

(4 citation statements)

References 45 publications

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“…Kierzkowski and Kisiel (2017) studied occupancy levels and interferences in low-cost carriers. Schultz and Schmidt (2018) analyzed passenger acceptance of boarding order, arrival rates and seat loading factors, and reported rear-door boarding increases efficiency. Delcea et al (2018) used agent based modeling (ABM) to explore two-door boarding from apron.…”

Section: Related Workmentioning

confidence: 99%

“…Delcea et al (2018) used agent based modeling (ABM) to explore two-door boarding from apron. Schultz and Schmidt (2018) tested the impact of 'slip-slide seat' during passenger interactions while passing through. Wittmann (2019) explored boarding of four sets of passengers while taking into consideration passenger attributes such as speed, luggage etc.…”

Section: Related Workmentioning

confidence: 99%

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Exploring the Impact of COVID-19 on Aircraft Boarding Strategies Using Discrete Event Simulation

Qureshi,

Qureshi

2022

OSCM: An Int. Journal

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Aviation is one of the most severely impacted industries by COVID-19. The passenger boarding-process is not only a bottleneck but is also one of the riskiest processes for COVID-19 transmission. There is a need for a decision-support tool that can proactively test the impact of COVID-19 policies on the passenger boarding-process. We achieve this by developing an adaptable modeling approach to Discrete Event Simulation (DES) that simulates the process of boarding under different COVID-19 policies and boarding-strategies. DES model was created using time and motion studies, flightlogs and manuals. Programing-logic was created using n=29 subject-matter experts. As a demonstratorcase, we tested seven of the most common boarding-strategies under different COVID-19 stages: pre-COVID, COVID-19 stage 1 and 2. Preliminary-results show the COVID-19 transmission risk may be decreased with a trade-off: passenger-satisfaction may decrease due to an increase in boarding-time and waiting-time. Steffen's method was most-effective in minimizing COVID-19 risk but is the most difficult to implement. Reverse pyramid and Window Middle Aisle, while slightly less effective than Steffen's method, but overall, more-effective and easier to implement with minimal COVID-19 risk. For COVID-19 stage 1 and 2, boarding time increased up to 33% and 64%, respectively, in-comparison to baseline pre-pandemic conditions. Further, up to 1.5 and 6.6 seat and aisle interferences along with a jetway-seat time of up to 13 minutes were observed. The developed modeling approach serves as a direct response to ICAO's (International Civil Aviation Organization) need for a tool to proactively test and develop policies that minimize COVID-19 risk.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Exploring the Impact of COVID-19 on Aircraft Boarding Strategies Using Discrete Event Simulation

Qureshi,

Qureshi

2022

OSCM: An Int. Journal

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“…Examples of shallow ML models are the support vector machine (SVM) invented by Cortes and Vapnik [2] in 1995, and neural networks (NN). Numerous shallow ML models have been deployed in many research fields, including speech and natural language processing, computer vision, and public opinion mining [3][4][5][6][7]. The linear and nonlinear shallow models have gained impressive results in regression and prediction studies, including the prediction of airport passenger throughput with high accuracy [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Approach to Air Passenger Index Prediction: Mutual Information Principle and Support Vector Regression (MI-Svr) Blended Model

Xiong¹,

Cheng²,

Co³

et al. 2020

Preprint

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Air passenger traffic prediction is crucial for the effective operation of civil aviation airports. Despite some progress in this field, the prediction accuracy and methods need further improvement. This paper proposes an integrated approach to the prediction of air passenger index as follows. Firstly, the air passenger index is defined and classified by the K-means clustering method. Based on the mutual information (MI) principle, the information entropy is used to analyze and select the key influencing factors of air passenger travel. By incorporating the MI principle into the support vector regression (SVR) framework, this paper presents an innovative MI-SVR machine learning model used to predict the air passenger index. Finally, the proposed model is validated by passenger throughput data of the Shanghai Pudong International Airport, China. The experimental results prove the model feasibility and effectiveness by comparing them with conventional methods, such as ARIMA, LSTM, and other machine learning models, outperformed by the MI-SVR model. Besides, it is shown that the prediction effect of each model could be improved by introducing influencing factors based on mutual information. The main findings are considered instrumental to the airport operation and air traffic optimization.

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“…According to the data from China Airline in 2017, nearly half of their sequence flights suffered from propagated delays. The problems induced by aircraft, passengers [5], crew and/or airport resources in early flights may directly or indirectly affect other connecting flights [6]. Consequently, a congested airport may propagate delays to its connecting airports through the delayed flights, which eventually has an impact on a significant part of the entire network performance [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Network Modelling Approach to Flight Delay Propagation: Some Empirical Evidence from China

Zhang

Feng

et al. 2019

Sustainability

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This paper examines flight delay propagation in air transport networks. Delays add to additional costs, inefficiencies, and unsustainable development. An integrated flight-based susceptible-infected-susceptible (FSIS) model was developed to analyse the flight delay process from a network-based perspective. The probability of flight delay propagation was determined using a translog model. The model was applied to an airline network consisting of thirty-three routes involving three airlines. The results show that the propagation probability is network-related and varies across different routes. The variation is related to the flight frequencies at airports, route distances, scheduled buffer times, and the propagated delay time. Whereas buffer time has a greater impact on smaller airports, flight movement has a greater impact on larger airports. Having a better understanding of how delays happen can help the development of strategies to avoid them. This will lead to less costs, higher efficiencies, and more sustainable airport and airline development.

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Advancements in Passenger Processes at Airports from Aircraft Perspective

Cited by 5 publications

References 45 publications

Exploring the Impact of COVID-19 on Aircraft Boarding Strategies Using Discrete Event Simulation

Exploring the Impact of COVID-19 on Aircraft Boarding Strategies Using Discrete Event Simulation

An Integrated Approach to Air Passenger Index Prediction: Mutual Information Principle and Support Vector Regression (MI-Svr) Blended Model

A Network Modelling Approach to Flight Delay Propagation: Some Empirical Evidence from China

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