Multitask learning deep neural networks to combine revealed and stated preference data
Name
Wang_RP_SP.pdf
Description
Accepted version
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1011.7 KB
Format
Adobe PDF
Checksum (MD5)
a223752dd63ec8309e0ddb03d0025bdd
Author(s)
Wang, Shenhao
Wang, Qingyi
Zhao, Jinhua
Date Issued
December 2020
Journal
Journal of Choice Modelling
Publisher
Elsevier BV
Citation
Wang, Shenhao, Qingyi Wang and Jinhua Zhao. “Multitask learning deep neural networks to combine revealed and stated preference data.” Journal of Choice Modelling, 37 (December 2020): 100236 © 2020 The Author(s)
Version
Author's final manuscript
Abstract
It is an enduring question how to combine revealed preference (RP) and stated preference (SP) data to analyze individual choices. While the nested logit (NL) model is the classical way to address the question, this study presents multitask learning deep neural networks (MTLDNNs) as an alternative framework, and discusses its theoretical foundation, empirical performance, and behavioral intuition. We first demonstrate that the MTLDNNs are theoretically more general than the NL models because of MTLDNNs’ automatic feature learning, flexible regularizations, and diverse architectures. By analyzing the adoption of autonomous vehicles (AVs), we illustrate that the MTLDNNs outperform the NL models in terms of prediction accuracy but underperform in terms of cross-entropy losses. To interpret the MTLDNNs, we compute the elasticities and visualize the relationship between choice probabilities and input variables. The MTLDNNs reveal that AVs mainly substitute driving and ride hailing, and that the variables specific to AVs are more important than the socio-economic variables in determining AV adoption. Overall, this work demonstrates that MTLDNNs are theoretically appealing in leveraging the information shared by RP and SP and capable of revealing meaningful behavioral patterns, although its performance gain over the classical NL model is still limited. To improve upon this work, future studies can investigate the inconsistency between prediction accuracy and cross-entropy losses, novel MTLDNN architectures, regularization design for the RP-SP question, MTLDNN applications to other choice scenarios, and deeper theoretical connections between choice models and the MTLDNN framework.
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.jocm.2020.100236
