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Predicting Travel Time Variability for Cost-Benefit Analysis

Author

Listed:
  • Stefanie Peer

    (VU University Amsterdam)

  • Carl Koopmans

    (VU University Amsterdam)

  • Erik T. Verhoef

    (VU University Amsterdam)

Abstract

Unreliable travel times cause substantial costs to travelers. Nevertheless, they are not taken into account in many cost-benefit-analyses (CBA), or only in very rough ways. This paper aims at providing simple rules on how variability can be predicted, based on travel time data from Dutch highways. The paper uses two different concepts of travel time variability. They differ in their assumptions on information availability to drivers. The first measure is based on the assumption that, for a given road link and given time of the day, the expected travel time is constant across all working days (rough information: RI). In the second case, expected travel times are assumed to reflect day-specific factors such as weather conditions or weekdays (fine information: FI). For both definitions of variability, we find that the mean travel time is a good predictor of variability. On average, longer delays are associated with higher variability. However, the derivative of travel time variability with respect to delays is decreasing in delays. It can be shown that this result relates to differences in the relative shares of observed traffic 'regimes' (free-flow, congested, hyper-congested) in the mean delay. For most CBAs, no information on the relative shares of the traffic regimes is available. A non-linear model based on mean travel times can be used as an approximation.

Suggested Citation

  • Stefanie Peer & Carl Koopmans & Erik T. Verhoef, 2010. "Predicting Travel Time Variability for Cost-Benefit Analysis," Tinbergen Institute Discussion Papers 10-071/3, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20100071
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    File URL: https://papers.tinbergen.nl/10071.pdf
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    References listed on IDEAS

    as
    1. van Lint, J.W.C. & van Zuylen, Henk J. & Tu, H., 2008. "Travel time unreliability on freeways: Why measures based on variance tell only half the story," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(1), pages 258-277, January.
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    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Bergström, Anna & Krüger, Niclas A., 2013. "Modeling passenger train delay distributions: evidence and implications," Working papers in Transport Economics 2013:3, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    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. Matthias Sweet & Mengke Chen, 2011. "Does regional travel time unreliability influence mode choice?," Transportation, Springer, vol. 38(4), pages 625-642, July.
    4. Jeremy Webb & Max Briggs & Clevo Wilson, 2018. "Breaking automotive modal lock-in: a choice modelling study of Jakarta commuters," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 20(1), pages 47-68, January.
    5. Koster, Paul & Peer, Stefanie & Dekker, Thijs, 2015. "Memory, expectation formation and scheduling choices," Economics of Transportation, Elsevier, vol. 4(4), pages 256-265.
    6. Parry, Ian, 2015. "Designing Fiscal Policy to Address the External Costs of Energy," International Review of Environmental and Resource Economics, now publishers, vol. 8(1), pages 1-56, May.
    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. Tu, Huizhao & Li, Hao & van Lint, Hans & van Zuylen, Henk, 2012. "Modeling travel time reliability of freeways using risk assessment techniques," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1528-1540.
    9. 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).
    10. Stefanie Peer & Erik Verhoef & Jasper Knockaert & Paul Koster & Yin‐Yen Tseng, 2015. "Long‐Run Versus Short‐Run Perspectives On Consumer Scheduling: Evidence From A Revealed‐Preference Experiment Among Peak‐Hour Road Commuters," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(1), pages 303-323, February.
    11. Eric Kroes & Paul Koster & Stefanie Peer, 2018. "A practical method to estimate the benefits of improved road network reliability: an application to departing air passengers," Transportation, Springer, vol. 45(5), pages 1433-1448, September.
    12. Gubins, Sergejs & Verhoef, Erik T. & de Graaff, Thomas, 2012. "Welfare effects of road pricing and traffic information under alternative ownership regimes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1304-1317.
    13. Stefanie Peer & Erik Verhoef & Jasper Knockaert & Paul Koster & Yin-Yen Tseng, 2011. "Long-Run vs. Short-Run Perspectives on Consumer Scheduling: Evidence from a Revealed-Preference Experiment among Peak-Hour Road Commuters," Tinbergen Institute Discussion Papers 11-181/3, Tinbergen Institute, revised 25 Aug 2014.
    14. 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.
    15. repec:dgr:uvatin:20100091 is not listed on IDEAS
    16. 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.
    17. Bardal, Kjersti Granås & Mathisen, Terje Andreas, 2015. "Winter problems on mountain passes – Implications for cost-benefit analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 74(C), pages 59-72.
    18. Sjoerd van der Spoel & Chintan Amrit & Jos van Hillegersberg, 2017. "Predictive analytics for truck arrival time estimation: a field study at a European distribution centre," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5062-5078, September.
    19. Kim, Jiwon & Mahmassani, Hani S., 2015. "Compound Gamma representation for modeling travel time variability in a traffic network," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 40-63.
    20. Koopmans, Carl & Groot, Wim & Warffemius, Pim & Annema, Jan Anne & Hoogendoorn-Lanser, Sascha, 2013. "Measuring generalised transport costs as an indicator of accessibility changes over time," Transport Policy, Elsevier, vol. 29(C), pages 154-159.
    21. Kathrin Goldmann & Gernot Sieg, 2020. "Quantifying the phantom jam externality: The case of an Autobahn section in Germany," Working Papers 30, Institute of Transport Economics, University of Muenster.
    22. Alejandro Tirachini & David Hensher & Michiel Bliemer, 2014. "Accounting for travel time variability in the optimal pricing of cars and buses," Transportation, Springer, vol. 41(5), pages 947-971, September.
    23. de Jong, Gerard C. & Bliemer, Michiel C.J., 2015. "On including travel time reliability of road traffic in appraisal," Transportation Research Part A: Policy and Practice, Elsevier, vol. 73(C), pages 80-95.
    24. Eliasson, Jonas, 2019. "Modelling reliability benefits," MPRA Paper 94817, University Library of Munich, Germany.

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    More about this item

    Keywords

    Travel time variability; Cost-benefit analysis; Mean-variance approach;
    All these keywords.

    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

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