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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

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  • 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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    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. Guerrero, Thomas E. & Guevara, C. Angelo & Cherchi, Elisabetta & Ortúzar, Juan de Dios, 2022. "Characterizing the impact of discrete indicators to correct for endogeneity in discrete choice models," Journal of choice modelling, Elsevier, vol. 42(C).
    4. Koster, Paul & Peer, Stefanie & Dekker, Thijs, 2015. "Memory, expectation formation and scheduling choices," Economics of Transportation, Elsevier, vol. 4(4), pages 256-265.
    5. 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.
    6. Geng, Kexin & Wang, Yacan & Cherchi, Elisabetta & Guarda, Pablo, 2023. "Commuter departure time choice behavior under congestion charge: Analysis based on cumulative prospect theory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 168(C).
    7. 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.
    8. 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).
    9. Gurmesh Sihag & Praveen Kumar & Manoranjan Parida, 2023. "Development of a Machine-Learning-Based Novel Framework for Travel Time Distribution Determination Using Probe Vehicle Data," Data, MDPI, vol. 8(3), pages 1-18, March.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Dierkes, Maik & Germer, Stephan & Sejdiu, Vulnet, 2020. "Probability distortion, asset prices, and economic growth," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 84(C).
    15. Valido, Jorge & Socorro, M. Pilar & Medda, Francesca, 2020. "Airport capacity and entry deterrence: Low cost versus full service airlines," Economics of Transportation, Elsevier, vol. 22(C).
    16. 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.

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    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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