Travel time variability and rational inattention

Fosgerau, Mogens; Jiang, Gege

doi:10.1016/j.trb.2018.12.003

Cited by 18 publications

(3 citation statements)

References 31 publications

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“…We develop a choice model estimation framework with latent constructs that capture information heterogeneity within the data. The key difference between our work and previous literature is that we show how rational inattention can be framed as a flexible and extendable generative learning model that emulates the cognitive processes in human behaviour [13,14]. We postulate that realistic behavioural patterns can be modelled using a data-driven generative learning process and we estimate a model to represent the underlying heterogeneity of the data.…”

Section: Introductionmentioning

confidence: 87%

“…[21] investigated consumer inattention correlation with willingness-to-pay for fuel consumption. Recently, rational inattention has been found to work well in time variability problems in travel demand forecasting [14]. The theory of rational inattention seeks to endogenize the imperfect awareness about the circumstances [10].…”

Section: Rational Inattention Behaviourmentioning

confidence: 99%

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Information processing constraints in travel behaviour modelling: A generative learning approach

Wong,

Farooq

2019

Preprint

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Travel decisions tend to exhibit sensitivity to uncertainty and information processing constraints. These behavioural conditions can be characterized by a generative learning process. We propose a data-driven generative model version of rational inattention theory to emulate these behavioural representations. We outline the methodology of the generative model and the associated learning process as well as provide an intuitive explanation of how this process captures the value of prior information in the choice utility specification. We demonstrate the effects of information heterogeneity on a travel choice, analyze the econometric interpretation, and explore the properties of our generative model. Our findings indicate a strong correlation with rational inattention behaviour theory, which suggest that individuals may ignore certain exogenous variables and rely on prior information for evaluating decisions under uncertainty. Finally, the principles demonstrated in this study can be formulated as a generalized entropy and utility based multinomial logit model.

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Section: Introductionmentioning

confidence: 87%

Section: Rational Inattention Behaviourmentioning

confidence: 99%

Information processing constraints in travel behaviour modelling: A generative learning approach

Wong,

Farooq

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…From a behavioral standpoint, a similar approach to this paper is found in Fosgerau and Jiang (2019) and Jiang et al (2020), who incorporate a rational inattention model into the context of transportation networks. While choice aversion and rational inattention are interconnected in terms of information processing, we model a particular variant of costly decision-making in the form of aversion to increasing choices, rather than mutual information through observing a signal.…”

Section: Introductionmentioning

confidence: 91%

A Recursive Logit Model with Choice Aversion and Its Application to Transportation Networks

Knies¹,

Lorca²,

Melo³

2020

Preprint

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We introduce a route choice model that incorporates the notion of choice aversion in transportation networks. Formally, we propose a recursive logit model which incorporates a penalty term that accounts for the dimension of the choice set at each node of the network. We make three contributions. First, we show that our model overcomes the correlation problem between routes, a common pitfall of traditional logit models. In particular, our approach can be seen as an alternative to the class of models known as Path Size Logit (PSL). Second, we show how our model can generate violations of regularity in the path choice probabilities. In particular, we show that removing edges in the network can decrease the probability of some existing paths. Finally, we show that under the presence of choice aversion, adding edges to the network can increase the total cost of the system. In other words, a type of Braess's paradox can emerge even in the case of uncongested networks. We show that these phenomena can be characterized in terms of a parameter that measures users' degree of choice aversion.

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Airline Market Concentration in Europe

Grosche

2022

Progress in IS

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Travel time variability and rational inattention

Cited by 18 publications

References 31 publications

Information processing constraints in travel behaviour modelling: A generative learning approach

Information processing constraints in travel behaviour modelling: A generative learning approach

A Recursive Logit Model with Choice Aversion and Its Application to Transportation Networks

Airline Market Concentration in Europe

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