S
Stephane Hess
Researcher at University of Leeds
Publications - 364
Citations - 11161
Stephane Hess is an academic researcher from University of Leeds. The author has contributed to research in topics: Discrete choice & Mixed logit. The author has an hindex of 52, co-authored 345 publications receiving 9640 citations. Previous affiliations of Stephane Hess include Royal Institute of Technology & University of Sydney.
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On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice
TL;DR: The analysis shows that, in this application, the Modified Latin Hypercube Sampling (MLHS) outperforms each type of Halton sequence, making the MLHS method an appealing approach for simulation of travel demand models and simulation-based models in general.
Estimation of value of travel-time saving using mixed logit models
TL;DR: In this article, the authors present a Web of Science Record created on 2008-02-15, modified on 2017-10-16 for the TRANSP-OR-CONF-2006-037
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Estimation of value of travel-time savings using mixed logit models
TL;DR: In this paper, the authors discuss some of the issues that arise with the computation of the implied value of travel-time savings in the case of discrete choice models allowing for random taste heterogeneity.
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Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application
Stephane Hess,David Palma +1 more
TL;DR: An introduction to Apollo, a powerful new freeware package for R that aims to provide a comprehensive set of modelling tools for both new and experienced users, which incorporates numerous post-estimation tools.
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Assuring Finite Moments for Willingness to Pay in Random Coefficient Models
TL;DR: In this article, the authors identify a criterion to determine whether, with a given distribution for the cost coefficient, the distribution of WTP has finite moments, and they show that some popular distributions used for WTP in random coefficient models, including normal, truncated normal, uniform and triangular, imply infinite moments for the distribution, even if truncated or bounded at zero.