Modeling Absolute and Relative Cost Differences in Stochastic User Equilibrium Problem

Xu, Xiangdong; Chen, Anthony; Kitthamkesorn, Songyot; Yang, Hai; Lo, Hong Kam

doi:10.1016/j.trpro.2015.06.005

Cited by 10 publications

(8 citation statements)

References 31 publications

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“…Both these properties are unrealistic. Therefore, Xu et al (2015) provide a hybrid method where the choice probabilities have a MNL en MNW component; however, this hybrid method stems from a combined impedance function (i.e., it defines the choice probabilities based on the choice probability functions of logit and weibit). Although the model can be useful in practice, the model does not stem from a utility formulation with an additive and multiplicative error term (Section 3.1.3 discusses how the hybrid models would fit in the GMEV framework).…”

Section: Generalized Multivariate Extreme Value Modelsmentioning

confidence: 99%

“…, the element-wise multiplication of y A and y M . This will not lead to the approach of Xu et al (2015), since only one scale parameter will be introduced when the vector is applied on a generating function.…”

Section: Hybrid Approachmentioning

confidence: 99%

“…However, the hybrid model of Xu et al (2015) can be derived from either the A-or M-models from our framework by introducing an additional term in the systematic utility. In the additive form, the hybrid model of equation ( 35) is obtained with…”

Section: Hybrid Approachmentioning

confidence: 99%

“…where a logarithmic term is inserted to simulate the multiplicative model (Xu et al, 2015). In our framework, this can be represented with generating vector…”

Section: Hybrid Approachmentioning

confidence: 99%

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Generalized Multivariate Extreme Value Models for Explicit Route Choice Sets

Smits,

Pel,

Bliemer

et al. 2018

Preprint

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This paper analyses a class of route choice models with closed-form probability expressions, namely, Generalized Multivariate Extreme Value (GMEV) models. A large group of these models emerge from different utility formulas that combine systematic utility and random error terms. Twelve models are captured in a single discrete choice framework. The additive utility formula leads to the known logit family, being multinomial, path-size, paired combinatorial and link-nested. For the multiplicative formulation only the multinomial and path-size weibit models have been identified; this study also identifies the paired combinatorial and link-nested variations, and generalizes the path-size variant. Furthermore, a new traveller's decision rule based on the multiplicative utility formula with a reference route is presented. Here the traveller chooses exclusively based on the differences between routes. This leads to four new GMEV models. We assess the models qualitatively based on a generic structure of route utility with random foreseen travel times, for which we empirically identify that the variance of utility should be different from thus far assumed for multinomial probit and logit-kernel models. The expected travellers' behaviour and model-behaviour under simple network changes are analysed. Furthermore, all models are estimated and validated on an illustrative network example with long distance and short distance origin-destination pairs. The new multiplicative models based on differences outperform the additive models in both tests.

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Section: Generalized Multivariate Extreme Value Modelsmentioning

confidence: 99%

Section: Hybrid Approachmentioning

confidence: 99%

Section: Hybrid Approachmentioning

confidence: 99%

“…where a logarithmic term is inserted to simulate the multiplicative model (Xu et al, 2015). In our framework, this can be represented with generating vector…”

Section: Hybrid Approachmentioning

confidence: 99%

See 2 more Smart Citations

Generalized Multivariate Extreme Value Models for Explicit Route Choice Sets

Smits,

Pel,

Bliemer

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…• Correction term models add correction terms to the deterministic utilities / probability relations to adjust the choice probabilities in order to capture route correlations. Such models include: C-Logit (CL) (Cascetta et al, 1996), Path Size Correction Logit (PSCL) (Bovy et al, 2008), Path Size Logit (PSL) (Ben-Akiva & Ramming, 1998), Path Size Hybrid (Xu et al, 2015), and Path Size Weibit (Kitthamkesorn & Chen, 2013).…”

Section: Introductionmentioning

confidence: 99%