# Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks

arXiv: https://arxiv.org/abs/2010.11644 · HTML: https://mlsanity.com/paper/2010.11644/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 15 of 15)

- [Representing Random Utility Choice Models with Neural Networks](https://mlsanity.com/paper/2207.12877/) (2022)
- [Fairness-enhancing deep learning for ride-hailing demand prediction](https://mlsanity.com/paper/2303.05698/) (2023)
- [Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?](https://mlsanity.com/paper/2303.04204/) (2023)
- [Deep trip generation with graph neural networks for bike sharing system expansion](https://mlsanity.com/paper/2303.11977/) (2023)
- [Aligning LLM with human travel choices: a persona-based embedding learning approach](https://mlsanity.com/paper/2505.19003/) (2025)
- [Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization](https://mlsanity.com/paper/2404.14701/) (2024)
- [A prediction and behavioural analysis of machine learning methods for modelling travel mode choice](https://mlsanity.com/paper/2301.04404/) (2023)
- [Enhancing Travel Choice Modeling with Large Language Models: A Prompt-Learning Approach](https://mlsanity.com/paper/2406.13558/) (2024)
- [A variational autoencoder approach for choice set generation and implicit perception of alternatives in choice modeling](https://mlsanity.com/paper/2106.13319/) (2021)
- [Deep Learning in Computational Biology: Advancements, Challenges, and Future Outlook](https://mlsanity.com/paper/2310.03086/) (2023)
- [Images in Discrete Choice Modeling: Addressing Data Isomorphism in Multi-Modality Inputs](https://mlsanity.com/paper/2312.14724/) (2023)
- [Bayesian Deep Learning for Discrete Choice](https://mlsanity.com/paper/2505.18077/) (2025)
- [Graph neural networks for residential location choice: connection to classical logit models](https://mlsanity.com/paper/2507.21334/) (2025)
- [DeepLogit: A sequentially constrained explainable deep learning modeling approach for transport policy analysis](https://mlsanity.com/paper/2509.13633/) (2025)
- [Neural-Bayesian Structure Learning for Discrete Choice Modeling](https://mlsanity.com/paper/2608.25258/) (2026)

## Abstract (arXiv)

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 (δ, 1-δ) 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.