This study presents an Ordinal version of Residual Logit (Ordinal-ResLogit) model to investigate the ordinal responses. We integrate the standard ResLogit model into COnsistent RAnk Logits (CORAL) framework, classified as a binary classification algorithm, to develop a fully interpretable deep learning-based ordinal regression model. As the formulation of the Ordinal-ResLogit model enjoys the Residual Neural Networks concept, our proposed model addresses the main constraint of machine learning algorithms, known as black-box. Moreover, the Ordinal-ResLogit model, as a binary classification framework for ordinal data, guarantees consistency among binary classifiers. We showed that the resulting formulation is able to capture underlying unobserved heterogeneity from the data as well as being an interpretable deep learning-based model. Formulations for market share, substitution patterns, and elasticities are derived. We compare the performance of the Ordinal-ResLogit model with an Ordered Logit Model using a stated preference (SP) dataset on pedestrian wait time and a revealed preference (RP) dataset on travel distance. Our results show that Ordinal-ResLogit outperforms the traditional ordinal regression model. Furthermore, the results obtained from the Ordinal-ResLogit RP model show that travel attributes such as driving and transit cost have significant effects on choosing the location of non-mandatory trips. In terms of the Ordinal-ResLogit SP model, our results highlight that the road-related variables and traffic condition are contributing factors in the prediction of pedestrian waiting time such that the mixed traffic condition significantly increases the probability of choosing longer waiting times.

本研究提出了残差 Logit (Ordinal-ResLogit) 模型的序数版本来研究序数响应。我们将标准 ResLogit 模型集成到 COnsistent RANk Logits (CORAL) 框架中，归类为二元分类算法，以开发完全可解释的基于深度学习的序数回归模型。由于 Ordinal-ResLogit 模型的制定采用了残差神经网络的概念，我们提出的模型解决了机器学习算法的主要约束，即黑盒。此外，Ordinal-ResLogit模型作为序数数据的二元分类框架，保证了二元分类器之间的一致性。我们表明，所得的公式能够从数据中捕获潜在的未观察到的异质性，并且成为可解释的基于深度学习的模型。推导出市场份额、替代模式和弹性的公式。我们使用有关行人等待时间的陈述偏好 (SP) 数据集和有关行人距离的显示偏好 (RP) 数据集，将 Ordinal-ResLogit 模型与有序 Logit 模型的性能进行比较。我们的结果表明 Ordinal-ResLogit 优于传统的序数回归模型。此外，Ordinal-ResLogit RP模型的结果表明，驾驶和交通成本等出行属性对非强制出行地点的选择有显着影响。就 Ordinal-ResLogit SP 模型而言，我们的结果强调，道路相关变量和交通状况是预测行人等待时间的影响因素，因此混合交通状况显着增加了选择较长等待时间的概率。