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A study on passengers' airport choice behavior using hybrid choice model: A case study of Seoul metropolitan area,South Korea |
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| Affiliation: | 1. The Korea Transport Institute, 370 Sicheong-daero, Sejong-si, 339-007, South Korea;2. Korea Aerospace University, 200-1 Hwajeon-dong, Goyang-City, South Korea;1. Energy, Transportation and Environment Department, Deutsches Institut für Wirtschaftsforschung, Berlin, Germany;2. Technische Universität Berlin, Germany;3. Center for Tax Policy and Administration, OECD, Paris, France;4. Institut für Höhere Studien, Wien, Austria;1. The Korea Transport Institute, 370 Sicheong-daero, Sejong-si, 339-007, South Korea;2. Korea Aerospace University, 200-1 Hwajeon-dong, Goyang-City, South Korea;1. Department of Economics, Management, and Business Law, University of Bari, Italy;2. Institute for Transport Studies & Choice Modelling Centre, University of Leeds, United Kingdom;1. University of Cagliari — CRiMM, via San Giorgio 12, Cagliari 09124, Italy;2. Technical University of Denmark — DTU, Bygningstorvet 116B, Lyngby 2800, Denmark |
| Abstract: | Improving explanatory power is significantly important to understand variables that affect attitudes and perceptions in the decision process. This paper estimates not only tangible attributes but also intangible perceptions and attitudes using a hybrid-choice model to study air passengers' flight choice behavior. The empirical study was conducted for the choice behavior of air passengers at Seoul Metropolitan Area, South Korea. The analysis uses a two-level Nested Logit model in order to examine which factors have more effect on passengers’ choice of airport and airline simultaneously by using airport and airline choice attributes. The study also estimated the parameters in the equations relating the latent variable by using Structural Equation Model (SEM). The results indicate that the models with latent variables have improved Goodness-of-Fit when compared to classical discrete choice models and effectively capture psychological factors that affect choice behavior of passengers. |
| Keywords: | Discrete choice model Multinomial logit model Two-level nested logit model Latent variables Structural equation model (SEM) Hybrid choice model (HCM) |
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