论文标题
基于理论的残留神经网络：离散选择模型和深神网络的协同作用
Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks
论文作者
论文摘要
研究人员经常将数据驱动和理论驱动的模型视为旅行行为分析中的两个不同甚至相互冲突的方法。但是，这两种方法是高度互补的，因为数据驱动的方法更具预测性，但不容易解释和稳健，而理论驱动的方法更容易解释，更健壮，但预测性较低。本研究使用其互补性，设计了一个基于理论的残差神经网络（TB-RESNET）框架，该框架协同基于共享的效用解释协同离散选择模型（DCMS）和深神经网络（DNN）。 TB-Resnet框架很简单，因为它使用（$δ$，1- $δ$）权重来利用DCMS的简单性和DNNS的丰富性，并防止DCMS不适合DNNS和DNNS过度拟合。该框架也是灵活的：三个实例的结核响应实例是基于多项式logit模型（MNL-RESNETS），前景理论（PT-RESNETS）和双曲线折现（HD-Resnets）设计的，这些（HD-Resnets）在三个数据集上进行了测试。与纯DCM相比，TB响应提供了更高的预测准确性，并揭示了由于dnn组件在TB-Resnets中增强的效用函数，因此揭示了更丰富的行为机制。与纯DNN相比，TB-RESNET可以适度改善预测并显着改善解释和鲁棒性，因为TB-Resnets中的DCM分量可以稳定效用函数和输入梯度。总体而言，这项研究表明，通过在TB-Resnet框架下结合其效用规格来协同DCM和DNN既可行又可取。尽管仍然存在一些局限性，但此TB-Resnet框架是在DCM和DNN之间创造相互益处的重要第一步，以进行旅行行为建模，并在预测，解释和鲁棒性方面取得了关键的改进。
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.
