Investigating the Random Seat Boarding Method without Seat Assignments with Common Boarding Practices Using an Agent-Based Modeling

Camelia Delcea, Liviu-Adrian Cotfas, Mostafa Salari and R. John Milne
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Camelia Delcea: Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, Romania
Liviu-Adrian Cotfas: Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, Romania
Mostafa Salari: Department of Civil Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada
R. John Milne: David D. Reh School of Business, Clarkson University, 333 B.H. Snell Hall, Potsdam, NY 13699, USA

Sustainability, 2018, vol. 10, issue 12, 1-28

Abstract: Research related to creating new and improved airplane boarding methods has seen continuous advancement, in recent years, while most of the airline companies have remained committed to the traditional boarding methods. Among the most-used boarding methods, around the world, are back-to-front and random boarding with and without assigned seats. While the other boarding methods used in practice possess strict rules for passengers’ behavior, random without assigned seats is dependent on the passengers own way of choosing the “best” seats. The aim of this paper is to meticulously model the passengers’ behavior, especially, in random boarding without assigned seats and to test its efficiency in terms of boarding time and interferences, in comparison with the other commonly-adopted methods (random boarding with assigned seats, window-middle-aisle (WilMA), back-to-front, reverse pyramid, etc.). One of the main challenges in our endeavor was the identification of the real human passengers’ way of reasoning, when selecting their seats, and creating a model in which the agents possess preferences and make decisions, as close to those decisions made by the human passengers, as possible. We model their choices based on completed questionnaires from three hundred and eighty-seven human subjects. This paper describes the resulting agent-based model and results from the simulations.

Keywords: airplane boarding; agent-based modeling; human choice modeling; boarding strategies; NetLogo (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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