# Ordinal-ResLogit: Interpretable Deep Residual Neural Networks for Ordered Choices

arXiv: https://arxiv.org/abs/2204.09187 · HTML: https://mlsanity.com/paper/2204.09187/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 20 Apr 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Rank consistent ordinal regression for neural networks with application to age estimation](https://mlsanity.com/paper/1901.07884/) (2019)
- [Agreeing to Cross: How Drivers and Pedestrians Communicate](https://mlsanity.com/paper/1702.03555/) (2017)
- [Virtual Immersive Reality for Stated Preference Travel Behaviour Experiments: A Case Study of Autonomous Vehicles on Urban Roads](https://mlsanity.com/paper/1802.06180/) (2018)
- [Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning](https://mlsanity.com/paper/2002.07325/) (2020)

## Built on by (most cited first, 5 of 5)

- [Debiased machine learning for estimating the causal effect of urban traffic on pedestrian crossing behaviour](https://mlsanity.com/paper/2212.11322/) (2022)
- [Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning](https://mlsanity.com/paper/2606.24604/) (2026)
- [A deep causal inference model for fully-interpretable travel behaviour analysis](https://mlsanity.com/paper/2405.01708/) (2024)
- [From GEV to ResLogit: Spatially Correlated Discrete Choice Models for Pedestrian Movement Prediction](https://mlsanity.com/paper/2603.01325/) (2026)
- [Copula-ResLogit: A Deep-Copula Framework for Unobserved Confounding Effects](https://mlsanity.com/paper/2603.10284/) (2026)

## Abstract (arXiv)

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.