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A Rank-dependent Scheduling Model

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  • Paul Koster
  • Erik T. Verhoef

Abstract

This paper proposes an analytical framework for the scheduling decisions of road travellers that takes into account probability weighting using rank-dependent utility theory. The fundamental difference with the standard scheduling model based on expected utility is that the probabilities of arrivals are treated in a non-linear way. This paper shows how scheduling decisions are affected by the weighted probabilities of the traveller. We derive the costs of non-optimal chosen departure times because of probability weighting and show that if the parameterised probability weighting function is similar to what has been found for gambling, the costs of probability weighting for morning peak car travellers are around 3 per cent. For the full range of parameters tested, we find costs in the range of 0-24 per cent of total travel costs. © 2012 LSE and the University of Bath

Suggested Citation

  • Paul Koster & Erik T. Verhoef, 2012. "A Rank-dependent Scheduling Model," Journal of Transport Economics and Policy, University of Bath, vol. 46(1), pages 123-138, January.
  • Handle: RePEc:tpe:jtecpo:v:46:y:2012:i:1:p:123-138
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    References listed on IDEAS

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    1. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    2. Li, Zheng & Hensher, David A. & Rose, John M., 2010. "Willingness to pay for travel time reliability in passenger transport: A review and some new empirical evidence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(3), pages 384-403, May.
    3. Quiggin, John, 1982. "A theory of anticipated utility," Journal of Economic Behavior & Organization, Elsevier, vol. 3(4), pages 323-343, December.
    4. De Borger, Bruno & Fosgerau, Mogens, 2008. "The trade-off between money and travel time: A test of the theory of reference-dependent preferences," Journal of Urban Economics, Elsevier, vol. 64(1), pages 101-115, July.
    5. Wakker,Peter P., 2010. "Prospect Theory," Cambridge Books, Cambridge University Press, number 9780521765015.
    6. Frederick Mosteller & Philip Nogee, 1951. "An Experimental Measurement of Utility," Journal of Political Economy, University of Chicago Press, vol. 59, pages 371-371.
    7. Gijs Kuilen, 2009. "Subjective Probability Weighting and the Discovered Preference Hypothesis," Theory and Decision, Springer, vol. 67(1), pages 1-22, July.
    8. Fosgerau, Mogens & Karlström, Anders, 2010. "The value of reliability," Transportation Research Part B: Methodological, Elsevier, vol. 44(1), pages 38-49, January.
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    Citations

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    Cited by:

    1. Xiao, Yu & Fukuda, Daisuke, 2015. "On the cost of misperceived travel time variability," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 96-112.
    2. Paul Koster & Eric Pels & Erik Verhoef, 2016. "The User Costs of Air Travel Delay Variability," Transportation Science, INFORMS, vol. 50(1), pages 120-131, February.
    3. Gonzalez, Juan Marcos & Brett Hauber, A. & Reed Johnson, F., 2015. "Estimating conditional certainty equivalents using choice-experiment data," Journal of choice modelling, Elsevier, vol. 15(C), pages 14-25.
    4. Wang, Qian & Sundberg, Marcus & Karlström, Anders, 2013. "Scheduling choices under rank dependent utility maximization," Working papers in Transport Economics 2013:16, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    5. Eric Kroes & Paul R. Koster & Stefanie Peer, 2014. "A Practical Method to estimate the Benefits of Improved Network Reliability: An Application to Departing Air Passengers," Tinbergen Institute Discussion Papers 14-130/VIII, Tinbergen Institute.
    6. Koster, Paul & Peer, Stefanie & Dekker, Thijs, 2015. "Memory, expectation formation and scheduling choices," Economics of Transportation, Elsevier, vol. 4(4), pages 256-265.
    7. Zheng Li & David Hensher, 2013. "Behavioural implications of preferences, risk attitudes and beliefs in modelling risky travel choice with travel time variability," Transportation, Springer, vol. 40(3), pages 505-523, May.
    8. Thorhauge, Mikkel & Cherchi, Elisabetta & Rich, Jeppe, 2016. "How flexible is flexible? Accounting for the effect of rescheduling possibilities in choice of departure time for work trips," Transportation Research Part A: Policy and Practice, Elsevier, vol. 86(C), pages 177-193.
    9. Koster, Paul & Kroes, Eric & Verhoef, Erik, 2011. "Travel time variability and airport accessibility," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1545-1559.
    10. Jorge Valido & M. Pilar Socorro & Francesca Medda, 2013. "DYPES: Vertical differentiation, schedule delay and entry deterrence: Low cost vs. full service airlines," Working Papers 2013-05, FEDEA.
    11. Paul Koster & Hans Koster, 2013. "Analysing Heterogeneity in the Value of Travel Time and Reliability: A Semiparametric Estimation Approach," ERSA conference papers ersa13p1032, European Regional Science Association.

    More about this item

    JEL classification:

    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning
    • R49 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Other
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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