Neural-Bayesian Structure Learning (Neural-BSL) integrates differentiable structure learning with random-utility discrete choice estimation in a single differentiable framework. It keeps observed choices outside the graph to avoid distortion, learns attribute interactions through a structure-weighted network, and propagates interventions by updating attributes in topological order before recomputing utilities and choice probabilities. Evaluations on Seoul stated-preference and London revealed-preference data show Neural-BSL matches conventional benchmarks in predictive performance while uncovering behaviorally coherent dependency structures and revealing downstream traveler and trip adjustments under policy scenarios.
By Hyunsoo Yun, Eun Hak Lee, Jiaru Zhang, Ziran Wang, Eui-Jin Kim
arXiv:2505. 18077v3 Announce Type: replace-cross Abstract: Discrete choice models (DCMs) are used to analyze individual decision-making in contexts such as transportation choices, political elections, and consumer preferences.
By Daniel F. Villarraga, Ricardo A. Daziano
arXiv:2509. 07123v2 Announce Type: replace-cross Abstract: Generalized extreme value models capture dependence among choice alternatives in discrete choice modeling, but require this dependence to be predefined, symmetric, and shared uniformly across individuals.
By Yuqi Zhou, Zhanhong Cheng, Dingyi Zhuang, Lingqian Hu, Yuheng Bu, Shenhao Wang
arXiv:2609.08288v1 Announce Type: new
Abstract: Travel survey data are essential for transportation planning and travel behavior analysis, yet collecting large-scale representative samples is costly...
By Zijian Shen, Bin Zhou, Jiguang Wang, Ya Zhao, Jintao Ke
arXiv:2607. 13314v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) generate predictions on structured data via in-context learning, without task-specific estimation.
By Liu Liu, Dan Zhang
arXiv:2603. 24705v3 Announce Type: replace-cross Abstract: Discrete choice models are fundamental tools in management science, economics, and marketing for understanding and predicting decision-making.
By Easton Huch, Michael Keane
arXiv:2607. 28854v1 Announce Type: new Abstract: Discrete choice modeling is a common tool used for preference elicitation during policy-making, but this is typically done through parametric models.
By Sheng Lun Christine Cao, Destenie Nock, Alex Davis
arXiv:2606. 26432v1 Announce Type: new Abstract: Tabular foundation models achieve strong accuracy on choice prediction tasks, but their predictions often violate the economic logic those tasks require: raising a price can increase predicted demand, implied willingness-to-pay estimates are frequently negative or implausible, and unavailable alternatives receive nonzero probability.
By Yingshuo Wang, Xian Sun, Yanhang Li, Zhichao Fan, Zexin Zhuang
arXiv:2607. 03200v1 Announce Type: new Abstract: Origin-destination (OD) flow prediction is central to urban analytics, yet deep models trained on raw counts remain vulnerable to distribution shift.
By Changjian Liu, Yong Gao, Yuqing Wang, Leyi Su, Honglei Guo, Zhiyang Wang, Xiaoyu Wang, Fan Zhang
arXiv:2207. 12877v3 Announce Type: replace Abstract: Motivated by the successes of deep learning, we propose a class of neural network-based discrete choice models, called RUMnets, inspired by the random utility maximization (RUM) framework.
By Ali Aouad, Antoine D\'esir
arXiv:2603. 19139v2 Announce Type: replace Abstract: Learning systems must balance generalization across experiences with discrimination of task-relevant details.
By Ines Aitsahalia, Kiyohito Iigaya
The paper introduces a method for learning heterogeneous, individually conditioned utility functions—termed individuated utility—by leveraging rational choice theory. It presents a multi-stage architecture that estimates these functions from multimodal data and evaluates it on a large dataset of aesthetic judgments about automotive wheel designs. Results show that individuated models outperform universal utility models and foundation baselines, indicating that annotator disagreement reflects meaningful preference diversity.
By Shiwali Mohan, Matt Hong, Dule Shu, Aniek Fransen, Shabnam Hakimi, Matt Klenk