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.

在旅行行为分析中，研究人员经常将数据驱动模型和理论驱动模型视为两种完全不同甚至冲突的方法。但是，这两种方法是高度互补的，因为数据驱动的方法更具可预测性，但解释性和鲁棒性较低，而理论驱动的方法具有较高的解释性和鲁棒性，但预测性却较低。利用它们的互补性，本研究设计了基于理论的残差神经网络（TB-ResNet）框架，该框架基于共享选择的效用，将离散选择模型（DCM）和深度神经网络（DNN）协同工作。TB-ResNet框架很简单，因为它使用了（$\delta ，$ 1-$\delta$）加权以利用DCM的简单性和DNN的丰富性，并防止DCM拟合不足和DNN拟合过度。该框架也很灵活：基于多项式logit模型（MNL-ResNets），前景理论（PT-ResNets）和双曲线贴现（HD-ResNets）设计了三个TB-ResNets实例，并在三个数据集上进行了测试。与纯DCM相比，由于TB-ResNets中的DNN组件增强了效用功能，因此TB-ResNets提供了更高的预测准确性并揭示了更丰富的行为机制。与纯DNN相比，TB-ResNets可以适度地改善预测并显着改善解释和鲁棒性，因为TB-ResNets中的DCM组件可以稳定效用函数和输入梯度。全面的，这项研究表明，通过在TB-ResNet框架下结合DCM和DNN的实用程序规范，使DCM和DNN协同增效既可行又可取。尽管仍然存在一些局限性，但此TB-ResNet框架是重要的第一步，可在DCM和DNN之间为旅行行为建模创造互惠互利，并共同改善预测，解释和鲁棒性。