arXiv AI
LEBGen: An LLM-Enhanced Bayesian Network Framework for Few-Shot Travel Survey Data Generation
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arXiv AI
Sep 16
Generating Individual Travel Diaries Using Large Language Models Informed by Census and Land-Use Data
arXiv:2509.09710v3 Announce Type: replace-cross Abstract: This study introduces a Large Language Model (LLM) scheme for generating key attributes of travel diaries in agent-based transportation model...
arXiv AI
Aug 25
Benchmarking Retrieval-Augmented Generation Strategies for Large Language Model-Based Travel Mode Choice Prediction
The paper evaluates how Retrieval-Augmented Generation (RAG) can improve Large Language Model (LLM) predictions of travel mode choice. Four retrieval strategies—basic RAG, balanced retrieval, cross‑encoder re‑ranking, and a combination of balanced retrieval with cross‑encoder—are tested on three LLMs (GPT‑4o, o4‑mini, o3) using 2023 Puget Sound travel survey data. Results show that RAG boosts accuracy across models, with GPT‑4o plus balanced retrieval and cross‑encoder achieving 80.8% accuracy, surpassing traditional statistical and machine learning baselines and demonstrating strong zero‑shot transfer.
arXiv AI
Jun 12
TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation
arXiv:2606. 12657v1 Announce Type: new Abstract: Human mobility data is important for transportation, urban planning, and epidemic control, but large-scale trajectory collection is often costly and privacy-constrained, motivating realistic synthetic trajectory generation.
arXiv AI
Aug 18
Feasible and Novel Synthetic Population Generation with Tabular and Sequential Travel Attributes
arXiv:2608. 15867v1 Announce Type: cross Abstract: Synthetic populations are critical inputs for activity-based travel demand models, yet generating realistic populations from limited survey data remains challenging.
arXiv Machine Learning
Aug 24
Fine-tuning LLMs for Tourist Trajectory Prediction using Field Experiment Data
The paper demonstrates that fine‑tuning large language models (LLMs) on local tourist trajectory data can predict visitor movements under varying conditions. Using 566 trajectories from Wakayama Castle Park, Japan, the authors fine‑tuned Llama‑3.1‑8B, achieving 49.1% accuracy for next point‑of‑interest predictions and maintaining strong performance even on undersampled scenarios such as rainy days. This shows that LLMs can serve as high‑fidelity, context‑aware behavior models for tourist prediction and enable counterfactual analysis of mobility interventions.
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.