Airline choice model for an international round-trip flight considering outbound and return flight schedules
DOI:
https://doi.org/10.5604/01.3001.0014.2969Keywords:
round-trip, return flight, flight schedule interaction, international fligh, passengers' behaviourAbstract
This paper quantified the impact of outbound and return flight schedule preferences on airline choice for international trips. Several studies have used airline choice data to identify preferences and trade-offs of different air carrier service attributes, such as travel time, fare and flight schedule. However, estimation of the effect return flight schedules have on airline choice for an international round-trip flight has not yet been studied in detail. Therefore, this study introduces attributes related to return flight characteristics and round-trip flight schedule interaction into the airline choice models, which have not previously been reported in the literature. We developed a stated preference survey that includes round-trip fares based on flight schedule combinations and the number of days prior to departure fares was purchased. We applied modelling techniques using a set of stated preference data. A mixed logit model was tested for the presence of heterogeneity in passengers' preferences. Our results indicated that models with attributes related to return flight and its interaction with outbound flight attributes have a superior fit compared with models only based on attributes reported in the literature review. The model found shows that airfare, travel time, arrival preference schedule in the outward journey, departure preference in the return journey and the schedule combination of round-trip flight are significantly affecting passenger choice behaviour in international round-trip flights. Sensitivity analysis of airline service characteristics and their marketing implications are conducted. The analysis reports seven policies with the greatest impact on each airline choice probabilities. It shows that by reducing travel time and airfare and by adopting an afternoon and night schedule preference for outbound and return flight, respectively, the highest probability on airline choice would be reached. This research contributes to the current literature by enhancing the understanding of how passengers choose airlines, considering both outbound and inbound journey characteristics. Thus, this study provides an analytical tool designed to provide a better understanding of international round-trip flight demand determinants and support carrier decisions.
References
AEROCIVIL, 2017, Estadisticas operacionales del transporte aereo comercial. Bogota.
AKSOY, S., ATILGAN, E. AKINCI, S., 2003. Airline services marketing by domestic and foreign firms: Differences from the customers viewpoint. Journal of Air Transport Management, 9(6), 343–351. doi: 10.1016/S0969-6997(03)00034-6.
ALEX, A. P., MANJU, V. S. ISAAC, K. P., 2019. Modelling of travel behaviour of students using artificial intelligence. Archives of Transport, 51(3), 7-19. doi: 10.5604/01.3001.0013.6159.
ALTA, 2020. Alta traffic report, summary 2019. Available at: https://www.alta.aero/news/traffic-report/ alta-traffic-report-december-2019/ (Accessed: 14 May 2020).
BALCOMBE, K., FRASER, I., HARRIS, L., 2009. Consumer willingness to pay for in-flight service and comfort levels: A choice experiment. Journal of Air Transport Management, 15(5), 221–226. doi: 10.1016/j.jairtraman.2008.12.005.
BALOBABA, P., ODONI, A., BARNHART, C., 2015. The global airline industry. 2nd edn. Edited by John Wiley & Sons.
BEN-AKIVA, M., LERMAN, S. R., 1985. Discrete Choice Analysis. Theory and Application to Travel Demand. Cambridge. Massachusetts.: The MIT Press.
BREY, R., WALKER, J. L., 2011. Latent temporal preferences: An application to airline travel. Transportation Research Part A: Policy and Practice, 45(9), 880-895. doi: 10.1016/j.tra.2011.04.010.
CANTILLO, V., ORTÚZAR, J. D., WILLIAMS, H. C. W. L., 2007. Modeling discrete choices in the presence of inertia and serial correlation. Transportation Science, 41(2), 195–205. doi: 10.1287/trsc.1060.0178.
CHANG, L., SUN, P., 2012. Stated-choice analysis of willingness to pay for low cost carrier services. Journal of Air Transport Management, 20, 15-17. doi: 10.1016/j.jairtraman.2011.09.003.
CHOICEMETRICS, 2014. Ngene software, developed by Rose, John M.; Collins, Andrew T.; Bliemer, Michiel C.J.; Hensher, David A.
COCHRAN, W. G., 1977. Sampling Techniques. 3rd. Editi. New York: John Wiley and Sons.
DOMENCICH, T., MCFADDEN, D., 1975. Urban travel demand: A behavioral analysis, Amsterdam, Ed. North_Holland. doi: 10.1016/0041-1647(76)90063-0.
DRABAS, T., WU, C. L., 2013. Modelling air carrier choices with a Segment Specific Cross Nested Logit model. Journal of Air Transport Management, 32, 8–16. doi: 10.1016/j.jairtraman.2013.04.004.
ESPINO, R., MARTÍN, J. C., ROMÁN, C., 2008. Analyzing the effect of preference heterogeneity on willingness to pay for improving service quality in an airline choice context. Transportation Research Part E: Logistics and Transportation Review, 593–606. doi: 10.1016/j.tre.2007.05.007.
FLEISCHER, A., TCHETCHIK, A., TOLEDO, T., 2012. The Impact of Fear of Flying on Travelers. Flight Choice: Choice Model with Latent Variables’, Journal of Travel Research, 51(5), 653–663. doi: 10.1177/0047287512437856.
FREUND-FEINSTEIN, U., BEKHOR, S., 2017. An airline itinerary choice model that includes the option to delay the decision, Transportation Research Part A, 96, 64–78. doi: 10.1016/j.tra.2016.12.004.
GAO, Y., KOO, T. T. R., 2014. Flying Australia-Europe via China: A qualitative analysis of the factors affecting travellers: choice of Chinese carriers using online comments data. Journal of Air Transport Management, 39, 23–29. doi: 10.1016/j.jairtraman.2014.03.006.
GREENE, W. H., 2003. Econometric Analysis. fifth ed. Edited by Rod Banister. Upper Saddle River, New Jersey: Prentice Hall.
HENSHER, D. A., ROSE, J. M. GREENE, W. H., 2005. Applied Choice Analysis - A Primer. Cambridge: Cambridge University Press.
HESS, S., ADLER, T. POLAK, J. W., 2007. Modelling airport airline choice behaviour with the use of stated preference survey data. Transportation Research Part E: Logistics and Transportation Review, 43(3), 221-233. doi: 10.1016/j.tre.2006.10.002.
HESS, S. POLAK, J. W., 2005. Mixed logit modelling of airport choice in multi-airport regions. Journal of Air Transport Management, 11, 59-68. doi: 10.1016/j.jairtraman.2004.09.001.
HOSSAIN, I., SAQIB, N. U. HAQ, M. M., 2018. Scale heterogeneity in discrete choice experiment: An application of generalized mixed logit model in air travel choice. Economics Letters, 172, 85-88. doi: 10.1016/j.econlet.2018.08.037.
IATA, 2019. World Air Transport Statistics (WATS) 2019. Montreal.
JUHÁSZ, M., MÁTRAI, T. KOREN, C., 2017. Forecasting travel time reliability in Urban road transport. Archives of Transport, 43(3), 53-67. doi: 10.5604/01.3001.0010.4227.
JUNG, S.-Y. YOO, K. E., 2014. Passenger airline choice behavior for domestic short-haul travel in South Korea. Journal of Air Transport Management, 38, 43-47. doi: 10.1016/j.jairtraman.2013.12.017.
KOO, T. T. R., CAPONECCHIA, C. WILLIAMSON, A., 2018. How important is safety in making flight choices? Evidence from simple choice experiments. Transportation, 45(1), 159–175. doi: 10.1007/s11116-016-9730-6.
LEE, J.-K., KIM, S. H. SIM, G. R., 2019. Mode choice behavior analysis of air transport on the introduction of remotely piloted passenger aircraft. Journal of Air Transport Management, 76(July 2018), 48–55. doi: 10.1016/j.jairtraman.2019.02.007.
LEE, T. T.-H. YIP, T. L. 2017. Impact of Flight Departure Delay on Airline Choice Behavior. Asian Journal of Shipping and Logistics, 33(4), 273–278. doi: 10.1016/j.ajsl.2017.12.011.
DE LUCA, S., 2012. Modelling airport choice behaviour for direct flights, connecting flights and different travel plans. Journal of Transport Geography. Elsevier Ltd, 22, 148–163. doi: 10.1016/j.jtrangeo.2011.12.006.
LURKIN, V., GARROW, L.A., HIGGINS, M.J., NEWMAN, J.P., SCHYNS, M., 2017. Accounting for price endogeneity in airline itinerary choice models: An application to Continental U.S. markets. Transportation Research Part A: Policy and Practice, 100, 228–246. doi: 10.1016/j.tra.2017.04.007.
LURKIN, V., GARROW, L.A., HIGGINS, M.J., NEWMAN, J.P., SCHYNS, M., 2018. Modeling competition among airline itineraries. Transportation Research Part A: Policy and Practice, 113(March), 157-172. doi: 10.1016/j.tra.2018.04.001.
MÁRQUEZ, L., MACEA, L. F. SOTO, J. J. 2019. Willingness to change car use to commute to the UPTC main campus, Colombia : A hybrid discrete choice modeling approach. The Journal of Transport and Land Use, 12(1), 335-353.
MARTÍN, J. C., ROMÁN, C. ESPINO, R. 2011. Evaluating frequent flyer programs from the air passengers’ perspective. Journal of Air Transport Management, 17(6), 364–368. doi: 10.1016/j.jairtraman.2011.02.008.
MCFADDEN, D. TRAIN, K. 2000. Mixed MNL models for discrete response. Journal of Applied Econometrics, 15(5), 447-470. doi: 10.1002/1099-1255(200009/10)15:5<447:aidjae570>3.3.co;2-t.
MILIOTI, C. P., KARLAFTIS, M. G. AKKOGIOUNOGLOU, E., 2015. Traveler perceptions and airline choice: A multivariate probit approach. Journal of Air Transport Management, 49, 46-52. doi: 10.1016/j.jairtraman.2015.08.001.
MUMBOWER, S., GARROW, L. A. HIGGINS, M. J., 2014. Estimating flight-level price elasticities using online airline data: A first step toward integrating pricing, demand, and revenue optimization. Transportation Research Part A: Policy and Practice, 66(1), 196-212. doi: 10.1016/j.tra.2014.05.003.
ORTÚZAR, J. D. D. WILLUMSEN, L. G., 2011. Modelling Transport. Modelling Transport. doi: 10.1002/9781119993308.
PARK, J.-W., 2010. The effect of frequent flyer programs: A case study of the Korean airline industry. Journal of Air Transport Management, 16(5), 287-288. doi: 10.1016/j.jairtraman.2010.02.007.
PÉREZ, C., 2004. Técnicas de Análisis Multivariante de Datos Aplicaciones con SPSS. Madrid: Pearson Education.
PROUSSALOGLOU, K. KOPPELMAN, F. S., 1999. The choice of air carrier, flight, and fare class. Journal of Air Transport Management, 5(4), 193–201. doi: 10.1016/S0969-6997(99)00013-7.
ROSE, J.M., HENSHER, D.A., GREENE, W.H., WASHINGTON, S.P., 2012. Attribute exclusion strategies in airline choice: Accounting for exogenous information on decision maker processing strategies in models of discrete choice. Transportmetrica, 8(5), 344–360. doi: 10.1080/18128602.2010.506897.
SEELHORST, M. LIU, Y., 2015. Latent air travel preferences: Understanding the role of frequent flyer programs on itinerary choice. Transportation Research Part A: Policy and Practice, 80, 49–61. doi: 10.1016/j.tra.2015.07.007.
SZOLNOKI, G. HOFFMANN, D., 2013. Online, face-to-face and telephone surveys - Comparing different sampling methods in wine consumer research. Wine Economics and Policy, 2(2), 57–66. doi: 10.1016/j.wep.2013.10.001.
THEIS, G., ADLER, T.J., CLARKE, J.P., BENAKIVA M., 2006. Risk Aversion to Short Connections in Airline Itinerary Choice. Transportation Research Record: Journal of the Transportation Research Board, 1951, 28–36. doi: 10.3141/1951-04.
TRAIN, K. E., 2009. Discrete Choice Methods with Simulation. Second Ed. Cambridge: University of Cambridge.
TSAI, T. CHEN, C., 2019. Mixed logit analysis of trade-off effects between international air-line fares and fences : a revenue management perspective. Current Issues in Tourism, 22(3), 265–275. doi: 10.1080/13683500.2017.1402869.
WEN, C.-H., CHEN, T.-N. FU, C., 2014. A factor-analytic generalized nested logit model for determining market position of airlines. Transportation Research Part A: Policy and Practice, 62, 71–80. doi: 10.1016/j.tra.2014.02.001.
WEN, C.-H., CHEN, T.-N. HUANG, W.-W., 2009. Mixed Logit Analysis of International Airline Choice. Transportation Research Record: Journal of the Transportation Research Board, 2106(1), 20–29. doi: 10.3141/2106-03.
Wen, C.-H. Lai, S.-C., 2010. Latent class models of international air carrier choice. Trans-portation Research Part E: Logistics and Trans-portation Review, 46(2), 211–221. doi: 10.1016/j.tre.2009.08.004.
WEN, C.-H., WU, W.-N. FU, C., 2017. Preferences for alternative travel arrangements in case of major flight delays: Evidence from choice experiments with prospect theory. Transport Policy, (February 2017), 1–9. doi: 10.1016/j.tranpol.2017.02.005.
WEN, C.-H. YEH, Y., 2017. Modeling air travelers choice of flight departure and return dates on long holiday weekends. Journal of Air Transport Management, 65, 220–225. doi: 10.1016/j.jairtraman.2017.06.016.
WORLD-BANK, 2019. Air transport, passenger carried. Available at: https://data.worldbank.org/indicator/IS.AIR.PSGR (Accessed: 13 May 2020).
WU, C.-L. SO, T. H. H., 2018. On the flight choice behaviour of business-purpose passengers in the Australian domestic air market. Journal of Air Transport Management, 72(July), 56–67. doi: 10.1016/j.jairtraman.2018.07.006.
YANG, C.-W., LU, J.-L. HSU, C.-Y., 2014. Modeling joint airport and route choice behavior for international and metropolitan airports. Journal of Air Transport Management, 39, 89–95. doi: 10.1016/j.jairtraman.2014.05.001.
YEN, J.-R. CHEN, J.-X., 2017. Modelling the preference for business charter in the cross-strait market. Journal of Air Transport Management, 64, 151–160. doi: 10.1016/j.jairtraman.2016.08.010.
ZHANG, Y., 2012. Are Chinese passengers willing to pay more for better air services?. Journal of Air Transport Management, 25, 5–7. doi: 10.1016/j.jairtraman.2012.02.003.
ŻOCHOWSKA, R., KAROŃ, G., SOBOTA, A., JANECKI, R., 2017. Selected aspects of the methodology of a household interview survey on an urban agglomeration scale with regard to its services. Scientific Journal of Silesian University of Technology. Series Transport, 95, 239–249. doi: 10.20858/sjsutst.2017.95.22.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Archives of Transport journal allows the author(s) to hold the copyright without restrictions.
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
