IDEAS home Printed from https://ideas.repec.org/a/eee/eejocm/v6y2013icp44-59.html
   My bibliography  Save this article

Stated response and multiple discrete-continuous choice models: Analyses of residuals

Author

Listed:
  • Jäggi, Boris
  • Weis, Claude
  • Axhausen, Kay W.

Abstract

In sophisticated transport models, choice modelling is used to capture a wide range of behaviour, such as mode choice, vehicle choice and route choice. A newly developed approach to improving realism is the multiple discrete-continuous extreme value (MDCEV) model, which allows researchers to model the allocation of continuous amounts of a consumer good. Before implementing this model in overall frameworks, it is important to determine the accuracy of the forecasting. In this paper, an MDCEV model of household fleet choice based on data collected in a stated adaptation survey is presented. The model was used to predict the annual mileage of households with regard to 17 different types of cars, and the results of that forecast were compared to the actual data by calculating the residuals. The residual analysis showed that the model performed significantly better than a completely random model, but the share of wrongly allocated mileage, 70% of the total, remained high. However, the results of only one model were not sufficient to assess the procedure. The differences between two submodels, one with and one without public transport, regarding the distribution of the residuals indicated that model specification has a significant influence on performance. Therefore, more work on forecasting additional MDCEV models was necessary to have a basis for comparison. We compared two further MDCEV models to obtain a fuller understanding of their performance.

Suggested Citation

  • Jäggi, Boris & Weis, Claude & Axhausen, Kay W., 2013. "Stated response and multiple discrete-continuous choice models: Analyses of residuals," Journal of choice modelling, Elsevier, vol. 6(C), pages 44-59.
  • Handle: RePEc:eee:eejocm:v:6:y:2013:i:c:p:44-59
    DOI: 10.1016/j.jocm.2013.04.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1755534513000109
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jocm.2013.04.005?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. Bhat, Chandra R. & Sen, Sudeshna, 2006. "Household vehicle type holdings and usage: an application of the multiple discrete-continuous extreme value (MDCEV) model," Transportation Research Part B: Methodological, Elsevier, vol. 40(1), pages 35-53, January.
    2. Pinjari, Abdul Rawoof & Bhat, Chandra R. & Hensher, David A., 2009. "Residential self-selection effects in an activity time-use behavior model," Transportation Research Part B: Methodological, Elsevier, vol. 43(7), pages 729-748, August.
    3. Rachel Copperman & Chandra Bhat, 2007. "An analysis of the determinants of children’s weekend physical activity participation," Transportation, Springer, vol. 34(1), pages 67-87, January.
    4. Erika Spissu & Abdul Pinjari & Chandra Bhat & Ram Pendyala & Kay Axhausen, 2009. "An analysis of weekly out-of-home discretionary activity participation and time-use behavior," Transportation, Springer, vol. 36(5), pages 483-510, September.
    5. Jaehwan Kim & Greg M. Allenby & Peter E. Rossi, 2002. "Modeling Consumer Demand for Variety," Marketing Science, INFORMS, vol. 21(3), pages 229-250, December.
    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. Bhat, Chandra R. & Mondal, Aupal & Asmussen, Katherine E. & Bhat, Aarti C., 2020. "A multiple discrete extreme value choice model with grouped consumption data and unobserved budgets," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 196-222.
    2. Bhat, Chandra R., 2018. "A new flexible multiple discrete–continuous extreme value (MDCEV) choice model," Transportation Research Part B: Methodological, Elsevier, vol. 110(C), pages 261-279.
    3. Jian, Sisi & Rashidi, Taha Hossein & Dixit, Vinayak, 2017. "An analysis of carsharing vehicle choice and utilization patterns using multiple discrete-continuous extreme value (MDCEV) models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 362-376.
    4. Ke Wang & Xin Ye & Ram M Pendyala & Yajie Zou, 2017. "On the development of a semi-nonparametric generalized multinomial logit model for travel-related choices," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-19, October.
    5. Sikder, Sujan & Pinjari, Abdul Rawoof, 2013. "The benefits of allowing heteroscedastic stochastic distributions in multiple discrete-continuous choice models," Journal of choice modelling, Elsevier, vol. 9(C), pages 39-56.
    6. Liao, Fanchao & Dissanayake, Dilum & Homem de Almeida Correia, Gonçalo, 2024. "Modelling the complementarity and flexibility between different shared modes available in smart electric mobility hubs (eHUBS)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 190(C).
    7. Basil Schmid & Milos Balac & Kay W. Axhausen, 2019. "Post-Car World: data collection methods and response behavior in a multi-stage travel survey," Transportation, Springer, vol. 46(2), pages 425-492, April.
    8. Ye, Xin & Garikapati, Venu M. & You, Daehyun & Pendyala, Ram M., 2017. "A practical method to test the validity of the standard Gumbel distribution in logit-based multinomial choice models of travel behavior," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 173-192.
    9. Saxena, Shobhit & Pinjari, Abdul Rawoof & Paleti, Rajesh, 2022. "A multiple discrete-continuous extreme value model with ordered preferences (MDCEV-OP): Modelling framework for episode-level activity participation and time-use analysis," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 259-283.
    10. Rodrigo J. Tapia & Gerard Jong & Ana M. Larranaga & Helena B. Bettella Cybis, 2021. "Exploring Multiple‐discreteness in Freight Transport. A Multiple Discrete Extreme Value Model Application for Grain Consolidators in Argentina," Networks and Spatial Economics, Springer, vol. 21(3), pages 581-608, September.
    11. Hackbarth, André & Madlener, Reinhard, 2018. "Combined Vehicle Type and Fuel Type Choices of Private Households: An Empirical Analysis for Germany," FCN Working Papers 17/2018, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised May 2019.

    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. Jian, Sisi & Rashidi, Taha Hossein & Dixit, Vinayak, 2017. "An analysis of carsharing vehicle choice and utilization patterns using multiple discrete-continuous extreme value (MDCEV) models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 362-376.
    2. Sikder, Sujan & Pinjari, Abdul Rawoof, 2013. "The benefits of allowing heteroscedastic stochastic distributions in multiple discrete-continuous choice models," Journal of choice modelling, Elsevier, vol. 9(C), pages 39-56.
    3. Rodrigo J. Tapia & Gerard Jong & Ana M. Larranaga & Helena B. Bettella Cybis, 2021. "Exploring Multiple‐discreteness in Freight Transport. A Multiple Discrete Extreme Value Model Application for Grain Consolidators in Argentina," Networks and Spatial Economics, Springer, vol. 21(3), pages 581-608, September.
    4. Bhat, Chandra R., 2008. "The multiple discrete-continuous extreme value (MDCEV) model: Role of utility function parameters, identification considerations, and model extensions," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 274-303, March.
    5. Abdul Rawoof Pinjari & Chandra R. Bhat, 2011. "Activity-based Travel Demand Analysis," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 10, Edward Elgar Publishing.
    6. Wu, Guoqiang & Hong, Jinhyun, 2022. "An analysis of the role of residential location on the relationships between time spent online and non-mandatory activity-travel time use over time," Journal of Transport Geography, Elsevier, vol. 102(C).
    7. Yuri Park & Hyunnam Kim & Jongsu Lee, 2009. "Model for Studying Commodity Bundling with a Focus on Consumer Preference," TEMEP Discussion Papers 200935, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Nov 2009.
    8. Ipek Sener & Chandra Bhat, 2012. "Modeling the spatial and temporal dimensions of recreational activity participation with a focus on physical activities," Transportation, Springer, vol. 39(3), pages 627-656, May.
    9. Annesha Enam & Karthik C. Konduri & Naveen Eluru & Srinath Ravulaparthy, 2018. "Relationship between well-being and daily time use of elderly: evidence from the disabilities and use of time survey," Transportation, Springer, vol. 45(6), pages 1783-1810, November.
    10. Pinjari, Abdul Rawoof & Bhat, Chandra, 2010. "A multiple discrete-continuous nested extreme value (MDCNEV) model: Formulation and application to non-worker activity time-use and timing behavior on weekdays," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 562-583, May.
    11. Pinjari, Abdul Rawoof, 2011. "Generalized extreme value (GEV)-based error structures for multiple discrete-continuous choice models," Transportation Research Part B: Methodological, Elsevier, vol. 45(3), pages 474-489, March.
    12. Pinjari, Abdul Rawoof & Augustin, Bertho & Sivaraman, Vijayaraghavan & Faghih Imani, Ahmadreza & Eluru, Naveen & Pendyala, Ram M., 2016. "Stochastic frontier estimation of budgets for Kuhn–Tucker demand systems: Application to activity time-use analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 88(C), pages 117-133.
    13. Ahn, Jiwoon & Jeong, Gicheol & Kim, Yeonbae, 2008. "A forecast of household ownership and use of alternative fuel vehicles: A multiple discrete-continuous choice approach," Energy Economics, Elsevier, vol. 30(5), pages 2091-2104, September.
    14. Jeong, Jaehoon & Seob Kim, Chang & Lee, Jongsu, 2011. "Household electricity and gas consumption for heating homes," Energy Policy, Elsevier, vol. 39(5), pages 2679-2687, May.
    15. Md Sami Hasnine & Khandker Nurul Habib, 2020. "Modelling the dynamics between tour-based mode choices and tour-timing choices in daily activity scheduling," Transportation, Springer, vol. 47(5), pages 2635-2669, October.
    16. Guoqiang Wu & Jinhyun Hong & Piyushimita Thakuriah, 2022. "Investigating the temporal changes in the relationships between time spent on the internet and non-mandatory activity-travel time use," Transportation, Springer, vol. 49(1), pages 213-235, February.
    17. Matthew Gentzkow, 2007. "Valuing New Goods in a Model with Complementarity: Online Newspapers," American Economic Review, American Economic Association, vol. 97(3), pages 713-744, June.
    18. Elias, Wafa & Katoshevski-Cavari, Rachel, 2014. "The role of socio-economic and environmental characteristics in school-commuting behavior: A comparative study of Jewish and Arab children in Israel," Transport Policy, Elsevier, vol. 32(C), pages 79-87.
    19. Pradeep Chintagunta & Jean-Pierre Dubé & Vishal Singh, 2003. "Balancing Profitability and Customer Welfare in a Supermarket Chain," Quantitative Marketing and Economics (QME), Springer, vol. 1(1), pages 111-147, March.
    20. Singh, Abhilash C. & Faghih Imani, Ahmadreza & Sivakumar, Aruna & Luna Xi, Yang & Miller, Eric J., 2024. "A joint analysis of accessibility and household trip frequencies by travel mode," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).

    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:eejocm:v:6:y:2013:i:c:p:44-59. 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.journals.elsevier.com/journal-of-choice-modelling .

    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.