01015555

Component

https://doi.org/10.3141/1921-05

http://scholar.google.com...Hess&publication_year=2005

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.

01015546

English

pp 35-43

2005

Transportation Research Record: Journal of the Transportation Research Board

0309093953

Print

References (30) ; Tables (4)

Algorithms; Mode choice; Monte Carlo method; Optimization; Parking; Revealed preferences; Stated preferences

Mixed logit models

Germany; United Kingdom

Highways; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Jan 18 2006 2:36PM

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