# Choice modelling in the age of machine learning -- discussion paper

arXiv: https://arxiv.org/abs/2101.11948 · HTML: https://mlsanity.com/paper/2101.11948/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 183 references that are in the map.
- First posted on arXiv 28 Jan 2021.

## Builds on (most cited first, 23 of 23)

- [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 on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)](https://mlsanity.com/paper/2003.12206/) (2020)
- [InterpretML: A Unified Framework for Machine Learning Interpretability](https://mlsanity.com/paper/1909.09223/) (2019)
- [A Survey on the Explainability of Supervised Machine Learning](https://mlsanity.com/paper/2011.07876/) (2020)
- [Explainable Machine Learning in Deployment](https://mlsanity.com/paper/1909.06342/) (2019)
- [Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges](https://mlsanity.com/paper/2010.09337/) (2020)
- [A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability](https://mlsanity.com/paper/2002.00922/) (2020)
- [Prediction of rare feature combinations in population synthesis: Application of deep generative modelling](https://mlsanity.com/paper/1909.07689/) (2019)
- [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)
- [Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models](https://mlsanity.com/paper/1906.03855/) (2019)
- [Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling](https://mlsanity.com/paper/1706.00505/) (2017)
- [Rethinking travel behavior modeling representations through embeddings](https://mlsanity.com/paper/1909.00154/) (2019)
- [A Differentially Private Multi-Output Deep Generative Networks Approach For Activity Diary Synthesis](https://mlsanity.com/paper/2012.14574/) (2020)
- [A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data](https://mlsanity.com/paper/1901.06415/) (2019)
- [Semi-supervised GANs to Infer Travel Modes in GPS Trajectories](https://mlsanity.com/paper/1902.10768/) (2019)
- [Variational Bayesian Inference for Mixed Logit Models with Unobserved Inter- and Intra-Individual Heterogeneity](https://mlsanity.com/paper/1905.00419/) (2019)
- [A Dirichlet Process Mixture Model of Discrete Choice](https://mlsanity.com/paper/1801.06296/) (2018)
- [Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms](https://mlsanity.com/paper/2012.12155/) (2020)

## Built on by (most cited first, 26 of 26)

- [Toward LLM-Agent-Based Modeling of Transportation Systems: A Conceptual Framework](https://mlsanity.com/paper/2412.06681/) (2024)
- [Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?](https://mlsanity.com/paper/2303.04204/) (2023)
- [Transformer Choice Net: A Transformer Neural Network for Choice Prediction](https://mlsanity.com/paper/2310.08716/) (2023)
- [Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choice](https://mlsanity.com/paper/2605.26559/) (2026)
- [Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization](https://mlsanity.com/paper/2404.14701/) (2024)
- [Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark](https://mlsanity.com/paper/2102.01130/) (2021)
- [Mind the Gap: Modelling Difference Between Censored and Uncensored Electric Vehicle Charging Demand](https://mlsanity.com/paper/2301.06418/) (2023)
- [A Neural Network Based Choice Model for Assortment Optimization](https://mlsanity.com/paper/2308.05617/) (2023)
- [Enhancing Travel Choice Modeling with Large Language Models: A Prompt-Learning Approach](https://mlsanity.com/paper/2406.13558/) (2024)
- [Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective](https://mlsanity.com/paper/2501.18282/) (2025)
- [Efficient Inference for Inverse Reinforcement Learning and Dynamic Discrete Choice Models](https://mlsanity.com/paper/2512.24407/) (2025)
- [Embedding Foundation Model Predictions in Discrete-Choice Models with Structural Guarantees](https://mlsanity.com/paper/2606.26432/) (2026)
- [A variational autoencoder approach for choice set generation and implicit perception of alternatives in choice modeling](https://mlsanity.com/paper/2106.13319/) (2021)
- [Incorporating Domain Knowledge in Deep Neural Networks for Discrete Choice Models](https://mlsanity.com/paper/2306.00016/) (2023)
- [Computer vision-enriched discrete choice models, with an application to residential location choice](https://mlsanity.com/paper/2308.08276/) (2023)
- [Delphos: A reinforcement learning framework for assisting discrete choice model specification](https://mlsanity.com/paper/2506.06410/) (2025)
- [Discrete-Choice Model with Generalized Additive Utility Network](https://mlsanity.com/paper/2309.16970/) (2023)
- [Images in Discrete Choice Modeling: Addressing Data Isomorphism in Multi-Modality Inputs](https://mlsanity.com/paper/2312.14724/) (2023)
- [Fully Data-driven but Interpretable Human Behavioural Modelling with Differentiable Discrete Choice Model](https://mlsanity.com/paper/2412.19403/) (2024)
- [Incorporating graph neural network into route choice model](https://mlsanity.com/paper/2503.02315/) (2025)
- [Can large language models assist choice modelling? Insights into prompting strategies and current models' capabilities](https://mlsanity.com/paper/2507.21790/) (2025)
- [Interpretable Machine Learning Models for Modal Split Prediction in Transportation Systems](https://mlsanity.com/paper/2203.14191/) (2022)
- [Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network](https://mlsanity.com/paper/2405.14079/) (2024)
- [DeepLogit: A sequentially constrained explainable deep learning modeling approach for transport policy analysis](https://mlsanity.com/paper/2509.13633/) (2025)
- [Workplace Location Choice Model based on Deep Neural Network](https://mlsanity.com/paper/2510.01723/) (2025)
- [Multitask Reinforcement Learning for Assisting Choice Model Specification](https://mlsanity.com/paper/2609.18441/) (2026)

## Abstract (arXiv)

Since its inception, the choice modelling field has been dominated by theory-driven modelling approaches. Machine learning offers an alternative data-driven approach for modelling choice behaviour and is increasingly drawing interest in our field. Cross-pollination of machine learning models, techniques and practices could help overcome problems and limitations encountered in the current theory-driven modelling paradigm, such as subjective labour-intensive search processes for model selection, and the inability to work with text and image data. However, despite the potential benefits of using the advances of machine learning to improve choice modelling practices, the choice modelling field has been hesitant to embrace machine learning. This discussion paper aims to consolidate knowledge on the use of machine learning models, techniques and practices for choice modelling, and discuss their potential. Thereby, we hope not only to make the case that further integration of machine learning in choice modelling is beneficial, but also to further facilitate it. To this end, we clarify the similarities and differences between the two modelling paradigms; we review the use of machine learning for choice modelling; and we explore areas of opportunities for embracing machine learning models and techniques to improve our practices. To conclude this discussion paper, we put forward a set of research questions which must be addressed to better understand if and how machine learning can benefit choice modelling.