  Researchers often treat data-driven and theory-driven models as two disparate
or even conflicting methods in travel behavior analysis. However, the two
methods are highly complementary because data-driven methods are more
predictive but less interpretable and robust, while theory-driven methods are
more interpretable and robust but less predictive. Using their complementary
nature, this study designs a theory-based residual neural network (TB-ResNet)
framework, which synergizes discrete choice models (DCMs) and deep neural
networks (DNNs) based on their shared utility interpretation. The TB-ResNet
framework is simple, as it uses a ($\delta$, 1-$\delta$) weighting to take
advantage of DCMs' simplicity and DNNs' richness, and to prevent underfitting
from the DCMs and overfitting from the DNNs. This framework is also flexible:
three instances of TB-ResNets are designed based on multinomial logit model
(MNL-ResNets), prospect theory (PT-ResNets), and hyperbolic discounting
(HD-ResNets), which are tested on three data sets. Compared to pure DCMs, the
TB-ResNets provide greater prediction accuracy and reveal a richer set of
behavioral mechanisms owing to the utility function augmented by the DNN
component in the TB-ResNets. Compared to pure DNNs, the TB-ResNets can modestly
improve prediction and significantly improve interpretation and robustness,
because the DCM component in the TB-ResNets stabilizes the utility functions
and input gradients. Overall, this study demonstrates that it is both feasible
and desirable to synergize DCMs and DNNs by combining their utility
specifications under a TB-ResNet framework. Although some limitations remain,
this TB-ResNet framework is an important first step to create mutual benefits
between DCMs and DNNs for travel behavior modeling, with joint improvement in
prediction, interpretation, and robustness.
