Tri-reference-point hypothesis development for airport ground access behaviors

Abstract

Studies have applied single-reference-point or safety margin hypotheses to examine how advanced traveler information affects travel behaviors. However, these theories may fail to fully capture the trade-offs among origin departure time, airport access time, and terminal processing time in terms of airport ground access behaviors. In this study, we developed a tri-reference-point hypothesis and assumed that the rate of change of utility may change at the air passenger’s preferred (PAT), earliest acceptable (EAT), and latest acceptable (LAT) airport arrival times. With an empirical data set collected from 304 passengers at Taipei Songshan Airport, the study examined the tri-reference-point hypothesis by analyzing airport ground access mode choice behaviors with a pooled framework that combined revealed and stated preferences. Moreover, the study developed four alternative specifications for schedule delay variables, assuming that air passengers used different reference points to determine relative gains and losses of the expected airport arrival time. The specifications included selecting both EAT and LAT as the zero-utility points (an indifference-band specification) and either one of PAT, EAT, and LAT as the single zero-utility point. Regardless of which specification was employed for schedule delay variables, the tri-reference-point hypothesis was generally supported. In particular, a significant difference of the rate of change of utility around PAT, EAT, and LAT was identified in the analysis results. When managing increasing road travel times and increasingly congested terminals, air passengers were more willing to retime their origin departure time to an earlier time than to switch their ground access mode. The implications of the analysis results for airport ground access management are discussed in the study.

This is a preview of subscription content, log in to check access.

Fig. 1

Adapted from “Dynamic commuter departure time choice under uncertainty” by Jou et al. (2008), Transportation Research Part A: Policy and Practice, 42, pp. 774–783

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Notes

  1. 1.

    Jou et al. (2008) defined the earliest acceptable arrival time and work start time as two reference points for commuters. The utility value of commuters would be positive if their actual arrival time was within the time interval anchored by these two reference points. The third reference point was not explicitly defined by Jou et al. (2008). However, unlike the traditional indifference band framework, Jou et al. (2008) further assumed a preferred arrival time when the utility value would reach the highest value when commuters arrive at the office at this time.

References

  1. Börjesson, M., Eliasson, J., Franklin, J.P.: Valuations of travel time variability in scheduling versus mean–variance models. Transp. Res. Part B: Methodol. 46, 855–873 (2012)

    Google Scholar 

  2. Bhat, C.R., Castelar, S.: A unified mixed logit framework for modeling revealed and stated preferences: formulation and application to congestion pricing analysis in the San Francisco Bay area. Transp. Res. Part B: Methodol. 36, 593–616 (2002)

    Google Scholar 

  3. Bierlaire, M.: PythonBiogeme: A Short Introduction. Transport and Mobility Laboratory, School of Architecture, Civil and Environmental Engineering. Ecole Polytechnique Fédérale de Lausanne, Switzerland (2016)

    Google Scholar 

  4. Box, G.E., Cox, D.R.: An analysis of transformations. J. R. Stat. Soc.: Ser. B (Methodol.) 26, 211–243 (1964)

    Google Scholar 

  5. Choo, S., You, S., Lee, H.: Exploring characteristics of airport access mode choice: a case study of Korea. Transp. Plann. Technol. 36, 335–351 (2013)

    Google Scholar 

  6. de Jong, G., Daly, A., Pieters, M., Vellay, C., Bradley, M., Hofman, F.: A model for time of day and mode choice using error components logit. Transp. Res. Part E: Logist. Transp. Rev. 39, 245–268 (2003)

    Google Scholar 

  7. de Palma, A., Picard, N.: Route choice decision under travel time uncertainty. Transp. Res. Part A: Policy Pract. 39, 295–324 (2005)

    Google Scholar 

  8. Gosling, G.D.: Airport Ground Access Mode Choice Models: A Synthesis of Airport Practice. Synthesis 5, Airport Cooperative Research Program. Transportation Research Board, Washington, D.C. (2008)

    Google Scholar 

  9. Gupta, S., Vovsha, P., Donnelly, R.: Air passenger preferences for choice of airport and ground access mode in the New York City metropolitan region. Transp. Res. Rec.: J. Transp. Res. Board 2042, 3–11 (2008)

    Google Scholar 

  10. Harvey, G.: Study of airport access mode choice. J. Transp. Eng. 112, 525–545 (1986)

    Google Scholar 

  11. Hedeker, D., Gibbons, R.D.: MIXOR: a computer program for mixed-effects ordinal regression analysis. Comput. Methods Programs Biomed. 49, 157–176 (1996)

    Google Scholar 

  12. Hensher, D.A., Rose, J.M., Greene, W.H.: Combining RP and SP data: biases in using the nested logit ‘trick’–contrasts with flexible mixed logit incorporating panel and scale effects. J. Transp. Geogr. 16, 126–133 (2008)

    Google Scholar 

  13. Hess, S., Polak, J.W.: Airport, airline and access mode choice in the San Francisco Bay area. Pap. Reg. Sci. 85, 543–567 (2006)

    Google Scholar 

  14. Hess, S., Polak, J.W., Daly, A., Hyman, G.: Flexible substitution patterns in models of mode and time of day choice: new evidence from the UK and the Netherlands. Transportation 34, 213–238 (2007)

    Google Scholar 

  15. Houston Airport System: George Bush Intercontinental Airport (IAH) (2018). Departure Retrieved April 28, 2020, from http://www.fly2houston.com/iah/depart/

  16. Jou, R.-C., Hensher, D.A., Hsu, T.-L.: Airport ground access mode choice behavior after the introduction of a new mode: a case study of Taoyuan International Airport in Taiwan. Transp. Res. Part E: Logist. Transp. Rev. 47, 371–381 (2011)

    Google Scholar 

  17. Jou, R.-C., Kitamura, R., Weng, M.-C., Chen, C.-C.: Dynamic commuter departure time choice under uncertainty. Transp. Res. Part A: Policy Pract. 42, 774–783 (2008)

    Google Scholar 

  18. Kahneman, D., Tversky, A.: Prospect theory: An analysis of decision under risk. In: MacLean, L.C., Ziemba, W.T. (eds.) Handbook of the Fundamentals of Financial Decision Making: Part I, pp. 99–127. World Scientific, Singapore (2013)

    Google Scholar 

  19. Koop, G.J., Johnson, J.G.: The use of multiple reference points in risky decision making. J. Behav. Decis. Mak. 25, 49–62 (2012)

    Google Scholar 

  20. Koppelman, F.S.: Non-linear utility functions in models of travel choice behavior. Transportation 10, 127–146 (1981)

    Google Scholar 

  21. Koster, P., Kroes, E., Verhoef, E.: Travel time variability and airport accessibility. Transp. Res. Part B: Methodol. 45, 1545–1559 (2011)

    Google Scholar 

  22. Landau, S., Weisbrod, G., Gosling, G., Williges, C., Pumphrey, M., Fowler, M.: Passenger Value of Time, Benefit-Cost Analysis, and Airport Capital Investment Decisions. Volume 1: Guidebook for Valuing User Time Savings in Airport Capital Investment Decision Analysis. The National Academies Press, Washington, D.C. (2015)

    Google Scholar 

  23. Li, Z., Tirachini, A., Hensher, D.A.: Embedding risk attitudes in a scheduling model: application to the study of commuting departure time. Transp. Sci. 46, 170–188 (2012)

    Google Scholar 

  24. Long, J.S.: Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks, CA: SAGE Publications, Inc. (1997)

  25. Mahmassani, H.S., Chang, G.-L.: On boundedly rational user equilibrium in transportation systems. Transp. Sci. 21, 89–99 (1987)

    Google Scholar 

  26. Mahmassani, H.S., Liu, Y.-H.: Dynamics of commuting decision behaviour under advanced traveller information systems. Transp. Res. Part C: Emerg. Technol. 7, 91–107 (1999)

    Google Scholar 

  27. McFadden, D.L.: Econometric analysis of qualitative response models. In: Griliches, Z., Intriligator, M.D. (eds.) Handbook of Econometrics, pp. 1395–1457. North-Holland, The Netherlands (2007)

    Google Scholar 

  28. Noland, R.B., Polak, J.W.: Travel time variability: a review of theoretical and empirical issues. Transport Rev. 22, 39–54 (2002)

    Google Scholar 

  29. Orme, B.: Sample size issues for conjoint analysis studies. Provo, UT, United States of America (2010)

    Google Scholar 

  30. Paleti, R., Vovsha, P.S., Givon, D., Birotker, Y.: Joint modeling of trip mode and departure time choices using revealed and stated preference data. Transp. Res. Rec.: J. Transp. Res. Board 2429, 67–78 (2014)

    Google Scholar 

  31. PASSME: Fast Airports, Stress-free Journeys (2018). Retrieved April 28, 2020, from https://passme.eu

  32. Pels, E., Nijkamp, P., Rietveld, P.: Access to and competition between airports: a case study for the San Francisco Bay area. Transp. Res. Part A: Policy Pract. 37, 71–83 (2003)

    Google Scholar 

  33. Qi, J., Sim, M., Sun, D., Yuan, X.: Preferences for travel time under risk and ambiguity: implications in path selection and network equilibrium. Transp. Res. Part B: Methodol. 94, 264–284 (2016)

    Google Scholar 

  34. Core Team, R.: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Australia (2019)

    Google Scholar 

  35. Rose, J.M., Bliemer, M.C.J.: Sample size requirements for stated choice experiments. Transportation 40, 1021–1041 (2013)

    Google Scholar 

  36. Senbil, M., Kitamura, R.: Reference points in commuter departure time choice: a prospect theoretic test of alternative decision frames. J. of Intell. Transp. Syst. 8, 19–31 (2004)

    Google Scholar 

  37. Small, K.A.: The scheduling of consumer activities: work trips. Am. Econ. Rev. 72, 467–479 (1982)

    Google Scholar 

  38. Taipei Songshan Airport: Developing the Blueprint for TSA Personalized Mobile Device Service Applications. Civil Aeronautics Administrations, Ministry of Transportation and Communcations, Taiwan Republic of China (2017)

    Google Scholar 

  39. Tam, M.L., Lam, W.H.K., Lo, H.P.: Modeling air passenger travel behavior on airport ground access mode choices. Transportmetrica 4, 135–153 (2008)

    Google Scholar 

  40. Thorhauge, M., Cherchi, E., Rich, J.: Building efficient stated choice design for departure time choices using the scheduling model: Theoretical considerations and practical implications. In: Proceedings of Aalborg Trafikdage 2014, Aalborg, Denmark (2014)

  41. Thorhauge, M., Cherchi, E., Rich, J.: How flexible is flexible? Accounting for the effect of rescheduling possibilities in choice of departure time for work trips. Transp. Res. Part A: Policy Pract. 86, 177–193 (2016)

    Google Scholar 

  42. Tsamboulas, D.A., Nikoleris, A.: Passengers’ willingness to pay for airport ground access time savings. Transp. Res. Part A: Policy Pract. 42, 1274–1282 (2008)

    Google Scholar 

  43. Wang, X.T., Johnson, J.G.: A tri-reference point theory of decision making under risk. J. Exp. Psychol. Gen. 141, 743 (2012)

    Google Scholar 

  44. Wu, C.-L.: Airline Operations and Delay Management: Insights from Airline Economics, Networks and Strategic Schedule Planning. Routledge, New York, USA (2016)

    Google Scholar 

  45. Xu, H., Zhou, J., Xu, W.: A decision-making rule for modeling travelers’ route choice behavior based on cumulative prospect theory. Transp. Res. Part C: Emerg. Technol. 19, 218–228 (2011)

    Google Scholar 

Download references

Acknowledgments

The authors are indebted to three anonymous reviewers for their insightful comments and suggestions. This study was sponsored by the ROC Ministry of Science and Technology (MOST 107-2410-H-009-034-MY3). Part of this manuscript was presented at the ATRS (Air Transport Research Society) 23rd World Conference in Amsterdam.

Author information

Affiliations

Authors

Contributions

The authors confirm contribution to the paper as follows: study conception and design: Y-SC and S-YT data collection: Y-SC and S-YT; analysis and interpretation of results: Y-SC; draft manuscript preparation: Y-SC. All authors reviewed the results and approved the final version of the manuscript.

Corresponding author

Correspondence to Yi-Shih Chung.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: Specifications and estimation results of the tri-reference-point model under earliest- and latest-acceptable-arrival-time-as-zero-utility assumptions

Appendix: Specifications and estimation results of the tri-reference-point model under earliest- and latest-acceptable-arrival-time-as-zero-utility assumptions

The utility change due to schedule delay variables under the EaZ assumption could be specified as follows:

$$ \Delta U_{qit} = \alpha_{SDE2} \left( {t^{e} - t_{it}^{a} } \right)SDE2_{it} + \alpha_{SDE1} \left( {t_{it}^{a} - t^{e} } \right)SDE1_{it} + \left( {\alpha_{SDE1} \left( {t^{p} - t^{e} } \right) + \alpha_{SDL1} \left( {t_{it}^{a} - t^{p} } \right)} \right)SDL1_{it} + \left( {\alpha_{SDE1} \left( {t^{p} - t^{e} } \right) + \alpha_{SDL1} \left( {t^{l} - t^{p} } \right) + \alpha_{SDL2} \left( {t_{it}^{a} - t^{l} } \right)} \right)SDL2_{it} $$
(10)

where \( SDE1_{it} \), \( SDE2_{it} \), \( SDL1_{it} \), and \( SDL2_{it} \) were four dummy variables, whose value was 1 if the expected airport arrival time fell in the extremely early, early, late, and extremely late segment, respectively, and 0 otherwise. Similarly, the utility change under the LaZ assumption could be specified as follows:

$$ \Delta U_{qit} = \left( {\alpha_{SDE2} \left( {t^{e} - t_{it}^{a} } \right) + \alpha_{SDE1} \left( {t^{p} - t^{e} } \right) + \alpha_{SDL1} \left( {t^{l} - t^{p} } \right)} \right)SDE2_{it} + \left( {\alpha_{SDE1} \left( {t^{p} - t_{it}^{a} } \right) + \alpha_{SDL1} \left( {t^{l} - t^{p} } \right)} \right)SDE1_{it} + \alpha_{SDL1} \left( {t^{l} - t_{it}^{a} } \right)SDL1_{it} + \alpha_{SDL2} \left( {t_{it}^{a} - t^{l} } \right)SDL2_{it} $$
(11)

The estimation results of these two models were summarized in Table 4.

Table 4 Tri-reference-point models: earliest and latest acceptable airport arrival time as zero utility

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chung, Y., Tu, S. Tri-reference-point hypothesis development for airport ground access behaviors. Transportation (2020). https://doi.org/10.1007/s11116-020-10125-9

Download citation

Keywords

  • Airport ground access
  • Reference point
  • Choice data combination
  • Departure time