  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
for both datasets. 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.
