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

TL;DR

UniMob is a novel unified human mobility generation model that leverages large language models and spatial embeddings to produce reliable mobility data across diverse cities, outperforming existing methods especially in data-scarce environments.

Contribution

The paper introduces UniMob, a comprehensive model combining LLM-powered planning, shared spatial embeddings, and diffusion-based generation for city-agnostic mobility data synthesis.

Findings

Achieves over 30% improvement over baselines in multiple metrics.

Demonstrates robustness in zero- and few-shot scenarios.

Verifies privacy-preserving capabilities and downstream task applicability.

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.