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Over-reporting vs. overreacting: Commuters’ perceptions of travel times

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  • Peer, Stefanie
  • Knockaert, Jasper
  • Koster, Paul
  • Verhoef, Erik T.

Abstract

In a large-scale, real-life peak avoidance experiment, we asked participants to provide estimates of their average in-vehicle travel time during their morning commute. After comparing the reported travel times with the actual corresponding travel times, we found that the average travel times were overstated by a factor of 1.5. We showed that driver- and link-specific characteristics partially explained these exaggerations. Using the stated and revealed preference data, we investigated whether the driver-specific reporting errors were consistent with the drivers’ scheduling behaviors in reality and in hypothetical choice experiments. In both cases, we found no robust evidence that drivers behave as if they misperceive travel times to a similar extent as those they misreported, thereby implying that the reported travel times did not represent the actual or perceived travel times in a truthful manner. The results of this study suggest that caution should be recommended when reported travel time data are used in an uncritical manner during transport research and when determining policy.

Suggested Citation

  • Peer, Stefanie & Knockaert, Jasper & Koster, Paul & Verhoef, Erik T., 2014. "Over-reporting vs. overreacting: Commuters’ perceptions of travel times," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 476-494.
    • Handle: RePEc:eee:transa:v:69:y:2014:i:c:p:476-494
    DOI: 10.1016/j.tra.2014.07.005
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    Cited by:

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    3. Milad Haghani & Michiel C. J. Bliemer & John M. Rose & Harmen Oppewal & Emily Lancsar, 2021. "Hypothetical bias in stated choice experiments: Part I. Integrative synthesis of empirical evidence and conceptualisation of external validity," Papers 2102.02940, arXiv.org.
    4. Peer, Stefanie & Knockaert, Jasper & Koster, Paul & Tseng, Yin-Yen & Verhoef, Erik T., 2013. "Door-to-door travel times in RP departure time choice models: An approximation method using GPS data," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 134-150.
    5. Haghani, Milad & Bliemer, Michiel C.J. & Rose, John M. & Oppewal, Harmen & Lancsar, Emily, 2021. "Hypothetical bias in stated choice experiments: Part I. Macro-scale analysis of literature and integrative synthesis of empirical evidence from applied economics, experimental psychology and neuroimag," Journal of choice modelling, Elsevier, vol. 41(C).
    6. 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.
    7. Varela, Juan Manuel Lorenzo & Börjesson, Maria & Daly, Andrew, 2018. "Quantifying errors in travel time and cost by latent variables," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 520-541.
    8. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Finite mixture (or latent class) modeling in transportation: Trends, usage, potential, and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 134-173.
    9. Krčál, Ondřej & Peer, Stefanie & Staněk, Rostislav & Karlínová, Bára, 2019. "Real consequences matter: Why hypothetical biases in the valuation of time persist even in controlled lab experiments," Economics of Transportation, Elsevier, vol. 20(C).
    10. Varela, Juan Manuel Lorenzo & Börjesson, Maria & Daly, Andrew, 2018. "Quantifying errors in travel time and cost by latent variables," Working papers in Transport Economics 2018:3, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    11. 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.
    12. Chakroborty, Partha & Pinjari, Abdul Rawoof & Meena, Jayant & Gandhi, Avinash, 2021. "A Psychophysical Ordered Response Model of Time Perception and Service Quality: Application to Level of Service Analysis at Toll Plazas," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 44-64.
    13. Ties Brands & Malvika Dixit & Edgard Zúñiga & Niels Oort, 2022. "Perceived and actual travel times in a multi-modal urban public transport network: comparing survey and AVL data," Public Transport, Springer, vol. 14(1), pages 85-103, March.
    14. Krčál, Ondřej & Peer, Stefanie & Staněk, Rostislav, 2021. "Can time-inconsistent preferences explain hypothetical biases?," Economics of Transportation, Elsevier, vol. 25(C).
    15. Mengying Cui & David Levinson, 2021. "Shortest paths, travel costs, and traffic," Environment and Planning B, , vol. 48(4), pages 828-844, May.
    16. Koster, Paul R. & Koster, Hans R.A., 2015. "Commuters’ preferences for fast and reliable travel: A semi-parametric estimation approach," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 289-301.
    17. Jasper Knockaert & Stefanie Peer & Erik Verhoef, 2016. "Identification of self-selection biases in field experiments using stated preference experiments," Natural Field Experiments 00568, The Field Experiments Website.
    18. Dixit, Vinayak & Jian, Sisi & Hassan, Asif & Robson, Edward, 2019. "Eliciting perceptions of travel time risk and exploring its impact on value of time," Transport Policy, Elsevier, vol. 82(C), pages 36-45.
    19. Lizana, Pedro & Ortúzar, Juan de Dios & Arellana, Julián & Rizzi, Luis I., 2021. "Forecasting with a joint mode/time-of-day choice model based on combined RP and SC data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 302-316.
    20. Heike Link & Dennis Gaus & Neil Murray & Maria Fernanda Guajardo Ortega & Flavien Gervois & Frederik von Waldow & Sofia Eigner, 2023. "Combining GPS Tracking and Surveys for a Mode Choice Model: Processing Data from a Quasi-Natural Experiment in Germany," Discussion Papers of DIW Berlin 2047, DIW Berlin, German Institute for Economic Research.
    21. Carlos Carrion & David Levinson, 2019. "Overestimation and underestimation of travel time on commute trips: GPS vs. self- reporting," Working Papers 2019-05, University of Minnesota: Nexus Research Group.
    22. Varotto, Silvia F. & Glerum, Aurélie & Stathopoulos, Amanda & Bierlaire, Michel & Longo, Giovanni, 2017. "Mitigating the impact of errors in travel time reporting on mode choice modelling," Journal of Transport Geography, Elsevier, vol. 62(C), pages 236-246.
    23. Yinger, John, 2021. "The price of access to jobs: Bid-function envelopes for commuting costs✰," Journal of Housing Economics, Elsevier, vol. 51(C).

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    More about this item

    Keywords

    Travel time perception; Reported travel times; Valuation of travel time; Scheduling choices; Panel latent class models; SP & RP data;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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