IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v46y2012i3p602-613.html
   My bibliography  Save this article

Strategic sampling for large choice sets in estimation and application

Author

Listed:
  • Lemp, Jason D.
  • Kockelman, Kara M.

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.

Suggested Citation

  • Lemp, Jason D. & Kockelman, Kara M., 2012. "Strategic sampling for large choice sets in estimation and application," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 602-613.
    • Handle: RePEc:eee:transa:v:46:y:2012:i:3:p:602-613
    DOI: 10.1016/j.tra.2011.11.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096585641100173X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tra.2011.11.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Frejinger, E. & Bierlaire, M. & Ben-Akiva, M., 2009. "Sampling of alternatives for route choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 43(10), pages 984-994, December.
    2. H C W L Williams, 1977. "On the Formation of Travel Demand Models and Economic Evaluation Measures of User Benefit," Environment and Planning A, , vol. 9(3), pages 285-344, March.
    3. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    4. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    5. Wen, Chieh-Hua & Koppelman, Frank S., 2001. "The generalized nested logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 627-641, August.
    6. Bierlaire, M. & Bolduc, D. & McFadden, D., 2008. "The estimation of generalized extreme value models from choice-based samples," Transportation Research Part B: Methodological, Elsevier, vol. 42(4), pages 381-394, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Leite Mariante, Gabriel & Ma, Tai-Yu & Van Acker, Véronique, 2018. "Modeling discretionary activity location choice using detour factors and sampling of alternatives for mixed logit models," Journal of Transport Geography, Elsevier, vol. 72(C), pages 151-165.
    2. Berjisian, Elmira & Habibian, Meeghat, 2019. "Developing a pedestrian destination choice model using the stratified importance sampling method," Journal of Transport Geography, Elsevier, vol. 77(C), pages 39-47.
    3. Clifton, Kelly J. & Singleton, Patrick A. & Muhs, Christopher D. & Schneider, Robert J., 2016. "Development of destination choice models for pedestrian travel," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 255-265.
    4. Guevara, C. Angelo & Ben-Akiva, Moshe E., 2013. "Sampling of alternatives in Logit Mixture models," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 185-198.
    5. Rezaei, Ali & Patterson, Zachary, 2018. "Preference stability in household location choice: Using cross-sectional data from three censuses," Research in Transportation Economics, Elsevier, vol. 67(C), pages 44-53.
    6. Bart Capéau & André Decoster & Gijs Dekkers, 2016. "Estimating and Simulating with a Random Utility Random Opportunity Model of Job Choice Presentation and Application to Belgium," International Journal of Microsimulation, International Microsimulation Association, vol. 9(2), pages 144-191.
    7. Decoster, André & Capéau, Bart, 2016. "Getting tired of work, or re-tiring in absence of decent job opportunities? Some insights from an estimated Random Utility/Random Opportunity model on Belgian data," EUROMOD Working Papers EM4/16, EUROMOD at the Institute for Social and Economic Research.
    8. Takanori Sakai & B. K. Bhavathrathan & André Alho & Tetsuro Hyodo & Moshe Ben-Akiva, 2020. "Commodity flow estimation for a metropolitan scale freight modeling system: supplier selection considering distribution channel using an error component logit mixture model," Transportation, Springer, vol. 47(2), pages 997-1025, April.
    9. Clifton, Kelly J. & Singleton, Patrick A. & Muhs, Christopher D. & Schneider, Robert J., 2016. "Representing pedestrian activity in travel demand models: Framework and application," Journal of Transport Geography, Elsevier, vol. 52(C), pages 111-122.
    10. C. Angelo Guevara & Caspar G. Chorus & Moshe E. Ben-Akiva, 2016. "Sampling of Alternatives in Random Regret Minimization Models," Transportation Science, INFORMS, vol. 50(1), pages 306-321, February.
    11. Ren, Xiyuan & Chow, Joseph Y.J., 2022. "A random-utility-consistent machine learning method to estimate agents’ joint activity scheduling choice from a ubiquitous data set," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 396-418.
    12. Agimass, Fitalew & Lundhede, Thomas & Panduro, Toke Emil & Jacobsen, Jette Bredahl, 2018. "The choice of forest site for recreation: A revealed preference analysis using spatial data," Ecosystem Services, Elsevier, vol. 31(PC), pages 445-454.
    13. Mohammad Nurul Hassan & Taha Hossein Rashidi & Neema Nassir, 2021. "Consideration of different travel strategies and choice set sizes in transit path choice modelling," Transportation, Springer, vol. 48(2), pages 723-746, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hamzeh Alizadeh & Bilal Farooq & Catherine Morency & Nicolas Saunier, 2018. "On the role of bridges as anchor points in route choice modeling," Transportation, Springer, vol. 45(5), pages 1181-1206, September.
    2. Stephane Hess & Denis Bolduc & John Polak, 2010. "Random covariance heterogeneity in discrete choice models," Transportation, Springer, vol. 37(3), pages 391-411, May.
    3. C. Angelo Guevara & Caspar G. Chorus & Moshe E. Ben-Akiva, 2016. "Sampling of Alternatives in Random Regret Minimization Models," Transportation Science, INFORMS, vol. 50(1), pages 306-321, February.
    4. Stephane Hess & Mark Fowler & Thomas Adler & Aniss Bahreinian, 2012. "A joint model for vehicle type and fuel type choice: evidence from a cross-nested logit study," Transportation, Springer, vol. 39(3), pages 593-625, May.
    5. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    6. Dam, Tien Thanh & Ta, Thuy Anh & Mai, Tien, 2022. "Submodularity and local search approaches for maximum capture problems under generalized extreme value models," European Journal of Operational Research, Elsevier, vol. 300(3), pages 953-965.
    7. de Jong, Gerard & Daly, Andrew & Pieters, Marits & van der Hoorn, Toon, 2007. "The logsum as an evaluation measure: Review of the literature and new results," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(9), pages 874-889, November.
    8. Fosgerau, Mogens & McFadden, Daniel & Bierlaire, Michel, 2010. "Choice probability generating functions," MPRA Paper 24214, University Library of Munich, Germany.
    9. Peter Davis & Pasquale Schiraldi, 2014. "The flexible coefficient multinomial logit (FC-MNL) model of demand for differentiated products," RAND Journal of Economics, RAND Corporation, vol. 45(1), pages 32-63, March.
    10. Stephane Hess & John W. Polak, 2004. "An analysis of parking behaviour using discrete choice models calibrated on SP datasets," ERSA conference papers ersa04p60, European Regional Science Association.
    11. Stijn Kelchtermans & Frank Verboven, 2010. "Participation and study decisions in a public system of higher education," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(3), pages 355-391.
    12. José-Benito Pérez-López & Margarita Novales & Francisco-Alberto Varela-García & Alfonso Orro, 2020. "Residential Location Econometric Choice Modeling with Irregular Zoning: Common Border Spatial Correlation Metric," Networks and Spatial Economics, Springer, vol. 20(3), pages 785-802, September.
    13. Hess, Stephane & Bierlaire, Michel & Polak, John W., 2005. "Estimation of value of travel-time savings using mixed logit models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(2-3), pages 221-236.
    14. Haghani, Milad & Sarvi, Majid & Shahhoseini, Zahra, 2015. "Accommodating taste heterogeneity and desired substitution pattern in exit choices of pedestrian crowd evacuees using a mixed nested logit model," Journal of choice modelling, Elsevier, vol. 16(C), pages 58-68.
    15. David Müller & Vladimir Shikhman, 2022. "Network manipulation algorithm based on inexact alternating minimization," Computational Management Science, Springer, vol. 19(4), pages 627-664, October.
    16. Tinessa, Fiore & Marzano, Vittorio & Papola, Andrea, 2020. "Mixing distributions of tastes with a Combination of Nested Logit (CoNL) kernel: Formulation and performance analysis," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 1-23.
    17. Shi, Haolun & Yin, Guosheng, 2018. "Boosting conditional logit model," Journal of choice modelling, Elsevier, vol. 26(C), pages 48-63.
    18. Kiran Tomlinson & Johan Ugander & Austin R. Benson, 2021. "Choice Set Confounding in Discrete Choice," Papers 2105.07959, arXiv.org, revised Aug 2021.
    19. Marzano, Vittorio & Papola, Andrea & Simonelli, Fulvio & Vitillo, Roberta, 2013. "A practically tractable expression of the covariances of the Cross-Nested Logit model," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 1-11.
    20. Tien Mai & Fabian Bastin & Emma Frejinger, 2018. "A decomposition method for estimating recursive logit based route choice models," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 7(3), pages 253-275, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transa:v:46:y:2012:i:3:p:602-613. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.