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Evaluation of advanced optimisation methods for estimating Mixed Logit models

Bastin, F, Cirillo, C and Hess, S (2006) Evaluation of advanced optimisation methods for estimating Mixed Logit models. Transportation Research Record, 1921. 35 - 43 . ISSN 0361-1981


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The performances of different simulation-based estimation techniques for mixed logit modeling are evaluated. A quasi-Monte Carlo method (modified Latin hypercube sampling) is compared with a Monte Carlo algorithm with dynamic accuracy. The classic Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization algorithm line-search approach and trust region methods, which have proved to be extremely powerful in nonlinear programming, are also compared. Numerical tests are performed on two real data sets: stated preference data for parking type collected in the United Kingdom, and revealed preference data for mode choice collected as part of a German travel diary survey. Several criteria are used to evaluate the approximation quality of the log likelihood function and the accuracy of the results and the associated estimation runtime. Results suggest that the trust region approach outperforms the BFGS approach and that Monte Carlo methods remain competitive with quasi-Monte Carlo methods in high-dimensional problems, especially when an adaptive optimization algorithm is used.

Item Type: Article
Copyright, Publisher and Additional Information: © 2006 National Academy of Sciences. This is an author produced version of a paper accepted for publication in Transportation Research Record. Uploaded in accordance with the publisher's self-archiving policy.
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 19 Mar 2012 11:27
Last Modified: 15 Sep 2014 11:17
Published Version: http://dx.doi.org/10.3141/1921-05
Status: Published
Publisher: National Academy of Sciences
Refereed: Yes
Identification Number: 10.3141/1921-05
URI: http://eprints.whiterose.ac.uk/id/eprint/43616

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