arXiv Machine Learning June 30, 2026 By Yuqi Zhou, Zhanhong Cheng, Dingyi Zhuang, Lingqian Hu, Yuheng Bu, Shenhao Wang

Alternative Graph Neural Networks: Synergizing GEV Models and Deep Learning for Travel Mode Choice Modeling

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

benchmarks

arXiv AI

Jul 22

GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models

arXiv:2607. 19270v1 Announce Type: cross Abstract: The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning.

By Alessandro Scalese, Santhanakrishnan Narayanan, Constantinos Antoniou

rag fine-tuning multimodal benchmarks

Hugging Face Trending Papers

Jul 21

GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models

The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning. While Graph Neural Networks have emerged as fast, data-driven surrogates, their practical deployment is severely constrained by a spatial generalization gap.

rag fine-tuning multimodal benchmarks

arXiv Machine Learning

Aug 5

Representing Random Utility Choice Models with Neural Networks

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

agents

arXiv AI

Jun 4

Smart Transportation Without Neurons -- Fair Metro Network Expansion with Tabular Reinforcement Learning

arXiv:2606. 04167v1 Announce Type: cross Abstract: We tackle the Metro Network Expansion Problem (MNEP), a subset of the Transport Network Design Problem (TNDP), which focuses on expanding metro systems to satisfy travel demand.

By Dimitris Michailidis, Sennay Ghebreab, Fernando P. Santos

reinforcement-learning safety

arXiv Machine Learning

Jul 10

Bayesian Deep Learning for Discrete Choice

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

safety

arXiv Machine Learning

Aug 27

Neural-Bayesian Structure Learning for Discrete Choice Modeling

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

benchmarks

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