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Latent temporal preferences: An application to airline travel

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  • Brey, Raúl
  • Walker, Joan L.

Abstract

An essential element of demand modeling in the airline industry is the representation of time of day demand--the demand for a given itinerary as a function of its departure or arrival times. It is an important datum that drives successful scheduling and fleet decisions. There are two key components to this problem: the distribution of the time of day demand and how preferred travel time influences itinerary choice. This paper focuses on estimating the time of day distribution. Our objective is to estimate it in a manner that is not confounded with air travel supply; is a function of the characteristics of the traveler, the trip, and the market; and accounts for potential measurement errors in self-reported travel time preferences. We employ a stated preference dataset collected by intercepting people who were booking continental US trips via an internet booking service. Respondents reported preferred travel times as well as choices from a hypothetical set of itineraries. We parameterize the time of day distribution as a mixture of normal distributions (due to the strong peaking nature of travel time preferences) and allow the mixing function to vary by individual characteristics and trip attributes. We estimate the time of day distribution and the itinerary choice model jointly in a manner that accounts for measurement error in the self-reported travel time preferences. We find that the mixture of normal distributions fits the time of day distribution well and is behaviorally intuitive. The strongest covariates of travel time preferences are party size and time zone change. The methodology employed to treat self-reported travel time preferences as potentially having error contributes to the broader transportation time of day demand literature, which either assumes that the desired travel times are known with certainty or that they are unknown. We find that the error in self-reported travel time preferences is statistically significant and impacts the inferred time of day demand distribution.

Suggested Citation

  • Brey, Raúl & Walker, Joan L., 2011. "Latent temporal preferences: An application to airline travel," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(9), pages 880-895, November.
  • Handle: RePEc:eee:transa:v:45:y:2011:i:9:p:880-895
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    References listed on IDEAS

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    Cited by:

    1. Gao, Yi & Koo, Tay T.R., 2014. "Flying Australia–Europe via China: A qualitative analysis of the factors affecting travelers' choice of Chinese carriers using online comments data," Journal of Air Transport Management, Elsevier, vol. 39(C), pages 23-29.
    2. Hansen, Mark & Liu, Yi, 2015. "Airline competition and market frequency: A comparison of the s-curve and schedule delay models," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 301-317.
    3. Mumbower, Stacey & Garrow, Laurie A. & Newman, Jeffrey P., 2015. "Investigating airline customers’ premium coach seat purchases and implications for optimal pricing strategies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 73(C), pages 53-69.
    4. Sanko, Nobuhiro & Hess, Stephane & Dumont, Jeffrey & Daly, Andrew, 2014. "Contrasting imputation with a latent variable approach to dealing with missing income in choice models," Journal of choice modelling, Elsevier, vol. 12(C), pages 47-57.
    5. Chiew, Esther & Daziano, Ricardo A. & Garrow, Laurie A., 2017. "Bayesian estimation of hazard models of airline passengers’ cancellation behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 96(C), pages 154-167.
    6. Seelhorst, Michael & Liu, Yi, 2015. "Latent air travel preferences: Understanding the role of frequent flyer programs on itinerary choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 80(C), pages 49-61.
    7. Bhat, Chandra R. & Dubey, Subodh K. & Nagel, Kai, 2015. "Introducing non-normality of latent psychological constructs in choice modeling with an application to bicyclist route choice," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 341-363.
    8. Lee, Misuk & Khelifa, Alexandre & Garrow, Laurie A. & Bierlaire, Michel & Post, David, 2012. "An analysis of destination choice for opaque airline products using multidimensional binary logit models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1641-1653.
    9. Pita, João P. & Adler, Nicole & Antunes, António P., 2014. "Socially-oriented flight scheduling and fleet assignment model with an application to Norway," Transportation Research Part B: Methodological, Elsevier, vol. 61(C), pages 17-32.

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