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

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  • 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
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    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.
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    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. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.

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