Liu, Yifan (University of California, Los Angeles), Liao, Xishun (University of California, Los Angeles), Ma, Haoxuan (University of California, Los Angeles), He, Brian Yueshuai (University of Louisville), Stanford, Chris (Novateur), Ma, Jiaqi (University of California, Los Angeles)

Human Mobility Modeling with Household Coordination Activities under Limited Information Via Retrieval-Augmented LLMs

Scheduled for presentation during the Invited Session "S18a-Innovative Applications of LLM in Multimodal Transportation Systems" (TH-LM-T18), Thursday, November 20, 2025, 11:50−12:10, Southport 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

Understanding human mobility patterns has long been a challenging task in transportation modeling. Due to the difficulties in obtaining high-quality training datasets across diverse locations, conventional activity-based models and learning-based human mobility modeling algorithms are particularly limited by the availability and quality of datasets. Current approaches primarily focus on spatial-temporal patterns while neglecting semantic relationships such as logical connections or dependencies between activities and household coordination activities like joint shopping trips or family meal times, both crucial for realistic mobility modeling. We propose a retrieval-augmented large language model (LLM) framework that generates activity chains with household coordination using only public accessible statistical and socio-demographic information, reducing the need for sophisticated mobility data. The retrieval-augmentation mechanism enables household coordination and maintains statistical consistency across generated patterns, addressing a key gap in existing methods. Our validation with NHTS and SCAG-ABM datasets demonstrates effective mobility synthesis and strong adaptability for regions with limited mobility data availability.