Study on Passenger Travel Behavior under Abnormal Flight Based on Regret Theory

DOI

10.2991/978-2-38476-230-9_19How to use a DOI?

Keywords

Abnormal Flights; Regret Theory; Behavior Choice; Multi-attribute Decision-making

Abstract

At present, there is a high occurrence of abnormal flights in China. It holds practical significance to investigate the decision-making process of passengers when faced with abnormal flight conditions, aiming to enhance service recovery and minimize airline losses. In this study, a model based on regret theory is developed to analyze passenger behavior selection when flights experience significant delays or cancellations on the Beijing-Shanghai and Beijing-Guangzhou routes. By calculating regret values for each route separately and classifying passengers accordingly, optimal solutions are determined for different scenarios of abnormal flight conditions. The actual choices made by passengers under such circumstances are obtained through questionnaires and compared with the theoretical model’s calculations. The findings demonstrate that the regret theory effectively explains passenger choice behavior.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xingyi Li
PY  - 2024
DA  - 2024/04/29
TI  - Study on Passenger Travel Behavior under Abnormal Flight Based on Regret Theory
BT  - Proceedings of the 4th International Conference on New Computational Social Science (ICNCSS 2024)
PB  - Atlantis Press
SP  - 159
EP  - 166
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-230-9_19
DO  - 10.2991/978-2-38476-230-9_19
ID  - Li2024
ER  -