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

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

doi:10.1016/j.tra.2014.07.005

Cited by 26 publications

(18 citation statements)

References 42 publications

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“…These higher values do not necessarily imply that the estimates are subject to hypothetical bias. Recent estimates by Peer et al (2014) using data from the same experiment show that the estimated values are close to estimates based on revealed preference data.…”

Section: Baseline Resultssupporting

confidence: 55%

Commuters’ preferences for fast and reliable travel: A semi-parametric estimation approach

Koster

2015

Transportation Research Part B: Methodological

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Section: Baseline Resultssupporting

confidence: 55%

Commuters’ preferences for fast and reliable travel: A semi-parametric estimation approach

Koster

2015

Transportation Research Part B: Methodological

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“…Moreover, detailed travel time data are available, which allow us to construct distinct long‐run and short‐run travel time expectations for all possible scheduling alternatives at a door‐to‐door level. We can thus avoid the use of reported travel time data, which are often imprecise or even biased (e.g., Small and Verhoef, , p. 21; Peer et al, ).…”

Section: Introductionmentioning

confidence: 99%

Long‐run Versus Short‐run Perspectives on Consumer Scheduling: Evidence From a Revealed‐preference Experiment Among Peak‐hour Road Commuters

Peer

Verhoef

Knockaert³

et al. 2015

Int Economic Review

Self Cite

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Earlier studies on scheduling behavior have mostly ignored that consumers have more flexibility to adjust their schedule in the long run than in the short run. We introduce the distinction between long‐run choices of travel routines and short‐run choices of departure times, using data from a real‐life peak avoidance experiment. We find that participants value travel time higher in the long‐run context, supposedly because changes in travel time can be exploited better through the adjustment of routines. Schedule delays are valued higher in the short run, reflecting that scheduling restrictions are typically more binding in the short run.

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“…Transport modelling typically uses either self-reported travel times and travel costs, or level of service times and cost calculated by a modelled transport network. In the latter approach, simplifications and heuristics such as the ones described in the previous section are used, whilst in the former misperceptions and self-justifications are likely to alter the true value of the reported variables (Peer et al, 2014) The impact of measurement errors in the input variables is best understood by comparing the distributions of self-reported and calculated travel times Figure 2 shows 'violin plots' for self-reported and calculated travel times for public transport and car. A violin plot is similar to a box plot, but more informative as it also shows the probability density of the data (it is always symmetric).…”

Section: Sources Of Measurement Errormentioning

confidence: 99%

Quantifying errors in travel time and cost by latent variables

Varela

Börjesson

Daly

2018

Transportation Research Part B: Methodological

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Travel time and travel cost are key variables for explaining travel behaviour and deriving the value of time. However, a general problem in transport modelling is that these variables are subject to measurement errors in transport network models. In this paper we show how to assess the magnitude of the measurement errors in travel time and travel cost by latent variables, in a large-scale travel demand model. The case study for Stockholm commuters shows that assuming multiplicative measurement errors for travel time and cost result in a better fit than additive ones; however, when measurement errors are modelled, the estimated time and cost parameters are robust to the modelling assumptions. Moreover, our results suggest that measurement errors in our dataset are larger for the travel cost than for the travel time, and that measurement errors are larger in self-reported travel time than software-calculated travel time for car-driver and car-passenger, and of similar magnitude for public transport. Among self-reported travel times, car-passenger has the largest errors, followed by car-driver and public transport, and for the softwarecalculated times, public transport exhibits larger errors than car. These errors, if not corrected, lead to biases in measures derived from the models, such as elasticity and values of travel time.

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

Cited by 26 publications

References 42 publications

Commuters’ preferences for fast and reliable travel: A semi-parametric estimation approach

Commuters’ preferences for fast and reliable travel: A semi-parametric estimation approach

Long‐run Versus Short‐run Perspectives on Consumer Scheduling: Evidence From a Revealed‐preference Experiment Among Peak‐hour Road Commuters

Quantifying errors in travel time and cost by latent variables

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