The value of travel time variance

Fosgerau, Mogens; Engelson, Leonid

doi:10.1016/j.trb.2010.06.001

Cited by 203 publications

(99 citation statements)

References 14 publications

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“…The use of this model to value travel time variability is due to Fosgerau and Engelson (2011) who used linear utility rates to find a cost measure that is linear in the mean and variance of the travel time distribution when ℎ is constant. In this case, the cost measure is additive over independent travel time components, and the additivity property was characterized in Engelson and Fosgerau (2011).…”

Section: Bibliographical Notesmentioning

confidence: 99%

“…The corresponding absolute measure could be defined as the planning time, which would be the buffer time, since the mean travel time enters the cost measure separately. Fosgerau and Engelson (2011) showed that a scheduling model with constant ℎ and affine leads to a mean-variance cost measure, where the coefficients 1 and 2 do not depend on the travel time distribution. Then the utility rates considered by Fosgerau and Engelson (2011) lead to a mean-dispersion measure of the kind considered in this paper.…”

Section: Type 3: Mean-dispersion Modelsmentioning

confidence: 99%

“…In some cases, scheduling preferences still lead to simple cost measures even when the departure time is restricted by a schedule. Fosgerau and Engelson (Fosgerau and Engelson 2011;Engelson and Fosgerau 2011) show that a constant utility rate ℎ at the origin and an affine or constant plus exponential utility rate at the destination lead to a mean-dispersion measure in the case of a scheduled service plus a term that depends on the headway of the scheduled service.…”

Section: Additivity and Homogeneitymentioning

confidence: 99%

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The cost of travel time variability: Three measures with properties

Engelson

Fosgerau

2016

Transportation Research Part B: Methodological

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This paper explores the relationships between three types of measures of the cost of travel time variability: measures based on scheduling preferences and implicit departure time choice, Bernoulli type measures based on a univariate function of travel time, and meandispersion measures. We characterise measures that are both scheduling measures and meandispersion measures and measures that are both Bernoulli and mean-dispersion. There are no measures that are both scheduling and Bernoulli. We consider the impact of requiring that measures are additive or homogeneous, proving also a new strong result on the utility rates in an additive scheduling measure. These insights are useful for selecting cost measures to use in applications.

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Section: Bibliographical Notesmentioning

confidence: 99%

Section: Type 3: Mean-dispersion Modelsmentioning

confidence: 99%

Section: Additivity and Homogeneitymentioning

confidence: 99%

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The cost of travel time variability: Three measures with properties

Engelson

Fosgerau

2016

Transportation Research Part B: Methodological

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“…The value commuters attach to a marginal reduction in travel time variability is referred to as the value of (travel time) reliability and can be inferred from observed scheduling choices (Fosgerau and Karlström, 2010;Fosgerau and Engelson, 2011). Typically, the value of reliability is derived using the presumption that commuters have rational expectations and an infinite memory.…”

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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“…Fosgerau and Karlström (2010) presented a model that estimates the value of travel time variability based on scheduling preferences. Fosgerau and Engelson (2011) considered the value of travel time variability under scheduling preferences that were defined in terms of linearly time-varying utility rates associated with being at the origin and at the destination. They have shown that a measure related to travel time variability is variance of travel time.…”

Section: Introductionmentioning

confidence: 99%

Estimating the value of travel time and of travel time reliability in road networks

Uchida

2014

Transportation Research Part B: Methodological

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This study proposes two network models which simultaneously estimate the value of travel time and of travel time reliability based on the risk-averse driver's route choice behaviour. The first model is formulated as a utility maximization problem under monotonic and separable link travel times, whereas the second model is formulated as a utility maximization problem under non-monotonic and non-separable link travel times. The proposed models have the same structure as a user equilibrium (UE) traffic assignment problem with elastic demand. It is shown that the first model, which addresses independent stochastic capacity, is formulated as an optimization problem with a unique solution and is solved by using an algorithm for a UE traffic assignment problem with fixed demand. The second model, which addresses both stochastic Origin-Destination (O-D) flow and stochastic link capacity, is formulated as a nonlinear complementary problem. O-D demand functions formulated in the proposed models are derived from the utility maximization behaviour of the driver in the network. Therefore, the network models proposed in this study are consistent with those of studies that address the value of travel time and of travel time reliability based on utility maximization behaviour without considering the driver's route choice. Numerical experiments are carried out to demonstrate the models presented in this study.Keywords: value of travel time, value of travel time reliability, road network INTRODUCTIONIn light of the need to evaluate travel time reliability in terms of its effect on mobility in transport networks, many studies have developed models that address uncertainties in the network. Uncertainties in the network can be categorized into the three main factors of supply, demand and travel behaviour. Studies on travel time reliability in the network began with those that address stochastic OriginDestination (O-D) demand flow. By assuming O-D demand flow that follows normal distributions, Asakura and Kashiwadani (1991) solved User Equilibrium (UE) traffic assignment problems several times by using a set of O-D demand flows that were sampled from normal distributions for the purpose of estimating travel time reliability. Clark and Watling (2005) proposed an equilibrium model that calculates travel time reliability under stochastic O-D demand flow when that flow follows a Poisson distribution. They employed probit-based Stochastic User Equilibrium (SUE) for the driver's route choice behaviour. They applied a method proposed by Isserlis (1918) for the purpose of calculating travel time reliability, i.e., variance of stochastic travel time. Nakayama and Takayama (2003) proposed an equilibrium model which assumes that O-D demand flow follows a binomial distribution. UE traffic assignment was employed for expressing the driver's route choice behaviour. They calculated travel time reliability by applying a moment-generating function. Zhou and Chen (2008) proposed an equilibrium model which assumes that O-D demand flow follows a...

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The value of travel time variance

Cited by 203 publications

References 14 publications

The cost of travel time variability: Three measures with properties

The cost of travel time variability: Three measures with properties

Memory, expectation formation and scheduling choices

Estimating the value of travel time and of travel time reliability in road networks

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