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Comparing and contrasting choice model and machine learning techniques in the context of vehicle ownership decisions

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  • Ali, Azam
  • Kalatian, Arash
  • Choudhury, Charisma F.

Abstract

In recent years, planners have started considering Machine Learning (ML) techniques as an alternative to discrete choice models (CM). ML techniques are primarily data-driven and typically achieve better prediction accuracy compared to CM. However, it is hypothesized that since the ML techniques do not have the strong grounding to economic theory as the CMs, they may not perform well in contexts that are radically different from the ‘training’ scenario. It is also hypothesized that the relative prediction performance may be affected by the metrics used for comparing the models.

Suggested Citation

  • Ali, Azam & Kalatian, Arash & Choudhury, Charisma F., 2023. "Comparing and contrasting choice model and machine learning techniques in the context of vehicle ownership decisions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    • Handle: RePEc:eee:transa:v:173:y:2023:i:c:s0965856423001477
    DOI: 10.1016/j.tra.2023.103727
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    1. Gakenheimer, Ralph, 1999. "Urban mobility in the developing world," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(7-8), pages 671-689.
    2. Lu, Jing & Meng, Yucan & Timmermans, Harry & Zhang, Anming, 2021. "Modeling hesitancy in airport choice: A comparison of discrete choice and machine learning methods," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 230-250.
    3. Storchmann, Karl, 2005. "Long-Run Gasoline demand for passenger cars: the role of income distribution," Energy Economics, Elsevier, vol. 27(1), pages 25-58, January.
    4. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, November.
    5. Youssef M. Aboutaleb & Mazen Danaf & Yifei Xie & Moshe Ben-Akiva, 2021. "Discrete Choice Analysis with Machine Learning Capabilities," Papers 2101.10261, arXiv.org.
    6. Wadud, Zia, 2020. "The effects of e-ridehailing on motorcycle ownership in an emerging-country megacity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 301-312.
    7. Lhéritier, Alix & Bocamazo, Michael & Delahaye, Thierry & Acuna-Agost, Rodrigo, 2019. "Airline itinerary choice modeling using machine learning," Journal of choice modelling, Elsevier, vol. 31(C), pages 198-209.
    8. Bhat, Chandra R. & Pulugurta, Vamsi, 1998. "A comparison of two alternative behavioral choice mechanisms for household auto ownership decisions," Transportation Research Part B: Methodological, Elsevier, vol. 32(1), pages 61-75, January.
    9. Hess, Stephane & Palma, David, 2019. "Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
    10. Joyce Dargay & Dermot Gately & Martin Sommer, 2007. "Vehicle Ownership and Income Growth, Worldwide: 1960-2030," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 143-170.
    11. Alwosheel, Ahmad & van Cranenburgh, Sander & Chorus, Caspar G., 2019. "‘Computer says no’ is not enough: Using prototypical examples to diagnose artificial neural networks for discrete choice analysis," Journal of choice modelling, Elsevier, vol. 33(C).
    12. Choudhary, Ravi & Vasudevan, Vinod, 2017. "Study of vehicle ownership for urban and rural households in India," Journal of Transport Geography, Elsevier, vol. 58(C), pages 52-58.
    13. Sifringer, Brian & Lurkin, Virginie & Alahi, Alexandre, 2020. "Enhancing discrete choice models with representation learning," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 236-261.
    14. Law, Teik Hua & Hamid, Hussain & Goh, Chia Ning, 2015. "The motorcycle to passenger car ownership ratio and economic growth: A cross-country analysis," Journal of Transport Geography, Elsevier, vol. 46(C), pages 122-128.
    15. Han, Yafei & Pereira, Francisco Camara & Ben-Akiva, Moshe & Zegras, Christopher, 2022. "A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 166-186.
    16. Yves Bentz & Dwight Merunka, 2000. "Neural networks and the multinomial logit for brand choice modelling: a hybrid approach," Post-Print hal-01822273, HAL.
    17. Sabreena Anowar & Naveen Eluru & Luis F. Miranda-Moreno, 2014. "Alternative Modeling Approaches Used for Examining Automobile Ownership: A Comprehensive Review," Transport Reviews, Taylor & Francis Journals, vol. 34(4), pages 441-473, July.
    18. Wang, Shenhao & Wang, Qingyi & Zhao, Jinhua, 2020. "Multitask learning deep neural networks to combine revealed and stated preference data," Journal of choice modelling, Elsevier, vol. 37(C).
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