Prediction of travel time variability for cost-benefit analysis

Peer, Stefanie; Koopmans, C.C.; Verhoef, Erik T.

doi:10.1016/j.tra.2011.09.016

Cited by 53 publications

(42 citation statements)

References 11 publications

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“…Another type of action that is intended to improve and optimize mobility in cities is the design of traffic management models (Gentile et al, 2007;Sundaram et al, 2011;Watling et al, 2012;Peer et al, 2012). These models involve simulation of the global dynamics of urban traffic through mathematical functions to obtain the volume of trips that traverse a particular area.…”

Section: State Of the Artmentioning

confidence: 99%

Green travellers? Exploring the spatial context of sustainable mobility styles

Barr

Prillwitz

2012

Applied Geography

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Section: State Of the Artmentioning

confidence: 99%

Green travellers? Exploring the spatial context of sustainable mobility styles

Barr

Prillwitz

2012

Applied Geography

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“…Hellinga (2011) Peer et al (2012) try to provide simple prediction rules for variability for road traffic. They are only looking at variations in travel times that are not expected by drivers.…”

Section: The Netherlandsmentioning

confidence: 99%

On including travel time reliability of road traffic in appraisal

Jong

Bliemer

2015

Transportation Research Part A: Policy and Practice

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In many countries, decision-making on proposals for national or regional infrastructure projects in passenger and freight transport includes carrying out a cost-benefit analysis for these projects. Reductions in travel times are usually a key benefit. However, if a project also reduces the variability U are usually not included in the cost-benefit analysis. To include reliability of travel or transport time in the cost-benefit analysis of infrastructure projects not only monetary values of reliability, but also reliability forecasting models are needed. As a result of an extensive feasibility study carried out for the German Federal Ministry of Transport, Building, and Urban Development this paper aims to provide a literature overview and outcomes of an expert panel on how best to calculate and monetise reliability benefits, synthesised into recommendations for implementing travel time reliability into existing transport models in the short, medium, and long term. The paper focuses on road transport, which has also been the topic for most of the available literature on modelling and valuing transport time reliability.

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“…Accordingly, β 0 , β 1 and γ 1 take the following values: β 0 = A C40 (p/hour), β 1 = A C8.86 (p/hour) and γ 1 = A C25.42 (p/hour). We use the empirical estimates of Peer et al (2012) to parametrize the distribution of travel times. We assume that f (T k |µ, σ 2 ) is log-normally distributed with an expected travel time of E(T 0 ) = µ = 1 3 , i.e.…”

Section: Parameter Assumptions and The Rational Expectations Outcomementioning

confidence: 99%

“…Namely, the benefits from improvements in travel time reliability in road-related transport projects amount to ca. 25% of the benefits related to travel time gains (Peer et al, 2012). Benefits from travel time gains, in turn, are estimated to constitute on average 60% of total user benefits in transport appraisals (Hensher, 2001).…”

Section: Introductionmentioning

confidence: 99%

Memory, expectation formation and scheduling choices

Koster

Peer

Dekker

2015

Economics of Transportation

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Limited memory capacity, retrieval constraints and anchoring are central to expectation formation processes. We develop a model of adaptive expectations where individuals are able to store only a finite number of past experiences of a stochastic state variable. Retrieval of these experiences is probabilistic and subject to error. We apply the model to scheduling choices of commuters and demonstrate that memory constraints lead to sub-optimal choices. We analytically and numerically show how memory-based adaptive expectations may substantially increase commuters' willingness-to-pay for reductions in travel time variability, relative to the rational expectations outcome.

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Prediction of travel time variability for cost-benefit analysis

Cited by 53 publications

References 11 publications

Green travellers? Exploring the spatial context of sustainable mobility styles

Green travellers? Exploring the spatial context of sustainable mobility styles

On including travel time reliability of road traffic in appraisal

Memory, expectation formation and scheduling choices

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