mlsanityData to 14 Oct 2026

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

arXiv 2101.11948 ↗First posted 28 Jan 2021econ.EM cs.LGcited by 26

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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.

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

23 of 183 references.

References · 23

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11 more
  1. 2019Prediction of rare feature combinations in population synthesis: Application of deep generative modellingcited by 19
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 1; 2023: 8; 2024: 5; 2025: 7; 2026: 3.

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  1. 2024Toward LLM-Agent-Based Modeling of Transportation Systems: A Conceptual Frameworkcited by 19
  2. 2023Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?cited by 8
  3. 2023Transformer Choice Net: A Transformer Neural Network for Choice Predictioncited by 6
  4. 2026Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choicecited by 6
  5. 2024Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularizationcited by 5
  6. 2021Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmarkcited by 4
  7. 2023Mind the Gap: Modelling Difference Between Censored and Uncensored Electric Vehicle Charging Demandcited by 3
  8. 2023A Neural Network Based Choice Model for Assortment Optimizationcited by 3
  9. 2024Enhancing Travel Choice Modeling with Large Language Models: A Prompt-Learning Approachcited by 3
  10. 2025Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspectivecited by 3
  11. 2025Efficient Inference for Inverse Reinforcement Learning and Dynamic Discrete Choice Modelscited by 3
  12. 2026Embedding Foundation Model Predictions in Discrete-Choice Models with Structural Guaranteescited by 3
14 more
  1. 2021A variational autoencoder approach for choice set generation and implicit perception of alternatives in choice modelingcited by 2
  2. 2023Incorporating Domain Knowledge in Deep Neural Networks for Discrete Choice Modelscited by 2
  3. 2023Computer vision-enriched discrete choice models, with an application to residential location choicecited by 2
  4. 2025Delphos: A reinforcement learning framework for assisting discrete choice model specificationcited by 2
  5. 2023Discrete-Choice Model with Generalized Additive Utility Networkcited by 1
  6. 2023Images in Discrete Choice Modeling: Addressing Data Isomorphism in Multi-Modality Inputscited by 1
  7. 2024Fully Data-driven but Interpretable Human Behavioural Modelling with Differentiable Discrete Choice Modelcited by 1
  8. 2025Incorporating graph neural network into route choice modelcited by 1
  9. 2025Can large language models assist choice modelling? Insights into prompting strategies and current models' capabilitiescited by 1
  10. 2022Interpretable Machine Learning Models for Modal Split Prediction in Transportation Systemscited by 0
  11. 2024Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Networkcited by 0
  12. 2025DeepLogit: A sequentially constrained explainable deep learning modeling approach for transport policy analysiscited by 0
  13. 2025Workplace Location Choice Model based on Deep Neural Networkcited by 0
  14. 2026Multitask Reinforcement Learning for Assisting Choice Model Specificationcited by 0