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Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data


  • Shenhao Wang
  • Qingyi Wang
  • Jinhua Zhao


It is an enduring question how to combine revealed preference (RP) and stated preference (SP) data to analyze travel behavior. This study presents a framework of multitask learning deep neural networks (MTLDNNs) for this question, and demonstrates that MTLDNNs are more generic than the traditional nested logit (NL) method, due to its capacity of automatic feature learning and soft constraints. About 1,500 MTLDNN models are designed and applied to the survey data that was collected in Singapore and focused on the RP of four current travel modes and the SP with autonomous vehicles (AV) as the one new travel mode in addition to those in RP. We found that MTLDNNs consistently outperform six benchmark models and particularly the classical NL models by about 5% prediction accuracy in both RP and SP datasets. This performance improvement can be mainly attributed to the soft constraints specific to MTLDNNs, including its innovative architectural design and regularization methods, but not much to the generic capacity of automatic feature learning endowed by a standard feedforward DNN architecture. Besides prediction, MTLDNNs are also interpretable. The empirical results show that AV is mainly the substitute of driving and AV alternative-specific variables are more important than the socio-economic variables in determining AV adoption. Overall, this study introduces a new MTLDNN framework to combine RP and SP, and demonstrates its theoretical flexibility and empirical power for prediction and interpretation. Future studies can design new MTLDNN architectures to reflect the speciality of RP and SP and extend this work to other behavioral analysis.

Suggested Citation

  • Shenhao Wang & Qingyi Wang & Jinhua Zhao, 2019. "Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data," Papers 1901.00227,, revised Aug 2019.
  • Handle: RePEc:arx:papers:1901.00227

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    References listed on IDEAS

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    3. Helveston, John Paul & Feit, Elea McDonnell & Michalek, Jeremy J., 2018. "Pooling stated and revealed preference data in the presence of RP endogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 70-89.
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    5. Hensher, David & Louviere, Jordan & Swait, Joffre, 1998. "Combining sources of preference data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 197-221, November.
    6. Brownstone, David & Bunch, David S. & Train, Kenneth, 2000. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 34(5), pages 315-338, June.
    7. Gregory K. Ingram, 1998. "Patterns of Metropolitan Development: What Have We Learned?," Urban Studies, Urban Studies Journal Limited, vol. 35(7), pages 1019-1035, June.
    8. Shenhao Wang & Qingyi Wang & Nate Bailey & Jinhua Zhao, 2018. "Deep Neural Networks for Choice Analysis: A Statistical Learning Theory Perspective," Papers 1810.10465,, revised Sep 2019.
    9. Shenhao Wang & Qingyi Wang & Jinhua Zhao, 2018. "Deep Neural Networks for Choice Analysis: Extracting Complete Economic Information for Interpretation," Papers 1812.04528,, revised Sep 2019.
    10. Ye, Xin & Pendyala, Ram M. & Gottardi, Giovanni, 2007. "An exploration of the relationship between mode choice and complexity of trip chaining patterns," Transportation Research Part B: Methodological, Elsevier, vol. 41(1), pages 96-113, January.
    11. Haghani, Milad & Sarvi, Majid, 2017. "Stated and revealed exit choices of pedestrian crowd evacuees," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 238-259.
    12. John C. Whitehead & Subhrendu K. Pattanayak & George L. Van Houtven & Brett R. Gelso, 2008. "Combining Revealed And Stated Preference Data To Estimate The Nonmarket Value Of Ecological Services: An Assessment Of The State Of The Science," Journal of Economic Surveys, Wiley Blackwell, vol. 22(5), pages 872-908, December.
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    Cited by:

    1. Melvin Wong & Bilal Farooq, 2019. "ResLogit: A residual neural network logit model," Papers 1912.10058,
    2. Yafei Han & Christopher Zegras & Francisco Camara Pereira & Moshe Ben-Akiva, 2020. "A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability," Papers 2002.00922,

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