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The overreliance on statistical goodness-of-fit and under-reliance on model validation in discrete choice models: A review of validation practices in the transportation academic literature

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  • Parady, Giancarlos
  • Ory, David
  • Walker, Joan

Abstract

An examination of model validation practices in the peer-reviewed transportation literature published between 2014 and 2018 reveals that 92% of studies reported goodness-of-fit statistics, and 64.6% reported some sort of policy-relevant inference analysis. However, only 18.1% reported validation performance measures, out of which 78% (14.2% of all studies) consisted of internal validation and 22% (4% of all studies) consisted of external validation. The proposition put forward in this paper is that the reliance on goodness-of-fit measures rather than validation performance is unwise, especially given the dependence of the transportation research field on observational (non-experimental) studies. Model validation should be a non-negotiable part of presenting a model for peer-review in academic journals. For that purpose, we propose a simple heuristic to select validation methods given the resources available to the researcher.

Suggested Citation

  • Parady, Giancarlos & Ory, David & Walker, Joan, 2021. "The overreliance on statistical goodness-of-fit and under-reliance on model validation in discrete choice models: A review of validation practices in the transportation academic literature," Journal of choice modelling, Elsevier, vol. 38(C).
  • Handle: RePEc:eee:eejocm:v:38:y:2021:i:c:s1755534520300543
    DOI: 10.1016/j.jocm.2020.100257
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    Citations

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    Cited by:

    1. Haghani, Milad & Bliemer, Michiel C.J. & Rose, John M. & Oppewal, Harmen & Lancsar, Emily, 2021. "Hypothetical bias in stated choice experiments: Part II. Conceptualisation of external validity, sources and explanations of bias and effectiveness of mitigation methods," Journal of choice modelling, Elsevier, vol. 41(C).
    2. Chung, Yi-Shih & Ku, Ya-Han, 2023. "Effect of time stress and store visibility on the dynamics of passenger activity choices at airport terminals based on indoor trajectory data," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    3. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Finite mixture (or latent class) modeling in transportation: Trends, usage, potential, and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 134-173.
    4. Krueger, Rico & Bierlaire, Michel & Daziano, Ricardo A. & Rashidi, Taha H. & Bansal, Prateek, 2021. "Evaluating the predictive abilities of mixed logit models with unobserved inter- and intra-individual heterogeneity," Journal of choice modelling, Elsevier, vol. 41(C).
    5. Łukawska, Mirosława & Paulsen, Mads & Rasmussen, Thomas Kjær & Jensen, Anders Fjendbo & Nielsen, Otto Anker, 2023. "A joint bicycle route choice model for various cycling frequencies and trip distances based on a large crowdsourced GPS dataset," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    6. Börjesson, Maria & Roberts, Christopher, 2023. "The impact of company cars on car ownership," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    7. Han, Chenglin & Luo, Lichen & Parady, Giancarlos & Takami, Kiyoshi & Chikaraishi, Makoto & Harata, Noboru, 2023. "Modeling joint eating-out destination choices incorporating group-level impedance: A case study of the Greater Tokyo Area," Journal of Transport Geography, Elsevier, vol. 111(C).
    8. Parady, Giancarlos & Frei, Andreas & Kowald, Matthias & Guidon, Sergio & Wicki, Michael & van den Berg, Pauline & Carrasco, Juan-Antonio & Arentze, Theo & Timmermans, Harry & Wellman, Barry & Takami, , 2021. "A comparative study of social interaction frequencies among social network members in five countries," Journal of Transport Geography, Elsevier, vol. 90(C).
    9. Gutierrez-Lythgoe, Antonio, 2023. "Autoempleo y Machine Learning: Una aplicación para España [Self-employment and Machine Learning: An application for Spain]," MPRA Paper 117275, University Library of Munich, Germany.
    10. Xinyi Wang & F. Atiyya Shaw & Patricia L. Mokhtarian & Kari E. Watkins, 2023. "Response willingness in consecutive travel surveys: an investigation based on the National Household Travel Survey using a sample selection model," Transportation, Springer, vol. 50(6), pages 2339-2373, December.
    11. P S, Karthika & Verma, Ashish, 2023. "Evaluating the gap choice decisions of pedestrians in conflict situations in mass religious gatherings and controlled experimental setup – A pilot study," Journal of choice modelling, Elsevier, vol. 49(C).
    12. Beeramoole, Prithvi Bhat & Arteaga, Cristian & Pinz, Alban & Haque, Md Mazharul & Paz, Alexander, 2023. "Extensive hypothesis testing for estimation of mixed-Logit models," Journal of choice modelling, Elsevier, vol. 47(C).
    13. Guarda, Pablo & Qian, Sean, 2024. "Statistical inference of travelers’ route choice preferences with system-level data," Transportation Research Part B: Methodological, Elsevier, vol. 179(C).
    14. Milos Balac & Sebastian Hörl & Basil Schmid, 2024. "Discrete choice modeling with anonymized data," Transportation, Springer, vol. 51(2), pages 351-370, April.
    15. Jia, Wenjian & Chen, T. Donna, 2023. "Investigating heterogeneous preferences for plug-in electric vehicles: Policy implications from different choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    16. Hasselwander, Marc & Bigotte, Joao F. & Antunes, Antonio P. & Sigua, Ricardo G., 2022. "Towards sustainable transport in developing countries: Preliminary findings on the demand for mobility-as-a-service (MaaS) in Metro Manila," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 501-518.
    17. Perez-Lopez, Jose-Benito & Novales, Margarita & Orro, Alfonso, 2022. "Spatially correlated nested logit model for spatial location choice," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 1-12.

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