Synthetic Human Mobility Data Generation: A Structured Review of Representations, Methods, and Practical Capabilities

2026-09-18

AI Summary

本文综述了合成人类移动数据生成方法，旨在解决真实数据受限问题，通过不同方法如机制模型、基于活动和代理的模拟及深度生成模型等来生成可用的合成数据。

Abstract

Human mobility data has become an increasingly important component of urban analytics. Although the range of available mobility data sources has expanded substantially, access remains highly constrained by commercial restrictions, privacy concerns, and institutional barriers. Data protection procedures also often reduce the analytical value of released datasets. Synthetic mobility data has emerged as a promising solution, but existing methods differ substantially in their underlying mechanisms, the information they preserve, the outputs they generate, and the analytical questions they can support. Their comparative strengths and trade-offs remain insufficiently understood for urban analytics. This paper presents a structured review of synthetic human mobility data generation from an urban analytics perspective. We review the literature by methodological family and index it by the mobility outputs each family generates natively and the analytical capabilities those outputs enable. We first provide a taxonomy of synthetic data products, including population and persona representations, activity schedules, trip and tour records, trajectories, and aggregate mobility patterns. We then review the major methodological families, spanning mechanistic models, survey-driven population synthesis, activity- and agent-based simulation, deep generative models, transformer-based mobility language models, and LLM-agentic systems. Building on this synthesis, we introduce a Meaning-Population-Autonomy framework that characterises these methods along three dimensions: behavioural meaning, population grounding and scale, and generation autonomy. We consider these dimensions the principal requirements for downstream urban analytics. Few methods deliver behavioural meaning, population grounding and autonomous generation at once, and fewer still with generation constrained to feasible trajectories.

Problem

• Human Mobility Data
• Urban Analytics
• Data Accessibility
• Privacy Concerns
• Synthetic Data

Innovation

• synthetic human mobility data
• urban analytics
• Meaning-Population-Autonomy framework
• deep generative models
• transformer-based mobility language models