All Cities are Equal: A Unified Human Mobility Generation Model Enabled by LLMs

2026-02-23

AI Summary

This work addresses the significant performance degradation of existing methods in generating human mobility trajectories in data-scarce cities, which hinders equitable cross-city modeling. To overcome this limitation, we propose UniMob—a unified cross-city human mobility generation framework that integrates a large language model–driven trip planner, a unified spatial embedding module, and a diffusion-based trajectory generator. UniMob is the first approach to enable zero-shot and few-shot trajectory generation without reliance on city scale or data availability, while preserving privacy and supporting diverse downstream tasks. Extensive experiments on five real-world urban datasets demonstrate that UniMob outperforms state-of-the-art methods by over 30% across multiple metrics, substantially enhancing model generalization, robustness, and practical applicability.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Synthetic human mobility generation is gaining traction as an ethical and practical approach to supporting the data needs of intelligent urban systems. Existing methods perform well primarily in data-rich cities, while their effectiveness declines significantly in cities with limited data resources. However, the ability to generate reliable human mobility data should not depend on a city's size or available resources, all cities deserve equal consideration. To address this open issue, we propose UniMob, a unified human mobility generation model across cities. UniMob is composed of three main components: an LLM-powered travel planner that derives high-level, temporally-aware, and semantically meaningful travel plans; a unified spatial embedding module that projects the spatial regions of various cities into a shared representation space; and a diffusion-based mobility generator that captures the joint spatiotemporal characteristics of human movement, guided by the derived travel plans. We evaluate UniMob extensively using two real-world datasets covering five cities. Comprehensive experiments show that UniMob significantly outperforms state-of-the-art baselines, achieving improvements of over 30\% across multiple evaluation metrics. Further analysis demonstrates UniMob's robustness in both zero- and few-shot scenarios, underlines the importance of LLM guidance, verifies its privacy-preserving nature, and showcases its applicability for downstream tasks.

Problem

• human mobility generation
• data-scarce cities
• urban inequality
• synthetic mobility data
• cross-city generalization

Innovation

• LLM-guided mobility generation
• unified spatial embedding
• diffusion-based trajectory synthesis
• cross-city generalization
• privacy-preserving synthetic data