Highlights ► Drawing choice samples based on standard logit probabilities is strategic. ► Strategic sampling provides more efficient estimates than SRS counterpart. ► New strategy requires very little additional computational effort or analyst capability.

    Abstract Many discrete choice contexts in transportation deal with large choice sets, including destination, route, and vehicle choices. Model estimation with large numbers of alternatives remains computationally expensive. In the context of the multinomial logit (MNL) model, limiting the number of alternatives in estimation by simple random sampling (SRS) yields consistent parameter estimates, but estimator efficiency suffers. In the context of more general models, such as the mixed MNL, limiting the number of alternatives via SRS yields biased parameter estimates. In this paper, a new, strategic sampling scheme is introduced, which draws alternatives in proportion to updated choice-probability estimates. Since such probabilities are not known a priori, the first iteration uses SRS among all available alternatives. The sampling scheme is implemented here for a variety of simulated MNL and mixed-MNL data sets, with results suggesting that the new sampling scheme provides substantial efficiency benefits. Thanks to reductions in estimation error, parameter estimates are more accurate, on average. Moreover, in the mixed MNL case, where SRS produces biased estimates (due to violation of the independence of irrelevant alternatives property), the new sampling scheme appears to effectively eliminate such biases. Finally, it appears that only a single iteration of the new strategy (following the initialization step using SRS) is needed to deliver the strategy’s maximum efficiency gains.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Strategic sampling for large choice sets in estimation and application


    Beteiligte:


    Erscheinungsdatum :

    2011-11-17


    Format / Umfang :

    12 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    USING CHOICE SETS FOR ESTIMATION AND PREDICTION IN ROUTE CHOICE

    Van Nes, Rob / Hoogendoorn-Lanser, Sascha / Koppelman, Frank S. | Taylor & Francis Verlag | 2008


    A strategic network choice model for global container flows: specification, estimation and application

    Tavasszy, Lóránt / Minderhoud, Michiel / Perrin, Jean-François et al. | Elsevier | 2011



    Estimating Route Choice Models from Stochastically Generated Choice Sets on Large-Scale Networks

    Vacca, Alessandro / Prato, Carlo Giacomo / Meloni, Italo | Transportation Research Record | 2019