Plan-to-Synthesis: Cross-City Human Mobility Generation via Semantic Latent Flow Matching

2026-09-26

AI Summary

This study addresses the poor generalizability and high transfer costs of single-city models in cross-city trajectory generation by proposing the SeMoFlow framework. This method pioneers a hierarchical semantic ID encoding scheme for heterogeneous points of interest (POIs) to construct a shared representation space, and adopts a "planning-synthesis" hierarchical paradigm. Specifically, an autoregressive planner generates macroscopic trajectory structures, while latent flow matching combined with decoding grounding techniques achieves high-fidelity microscopic synthesis. Experiments demonstrate that SeMoFlow significantly outperforms existing baselines on large-scale multi-city datasets. By effectively preserving city-specific patterns, the proposed framework enables efficient joint generation and seamless cross-city transfer.

Abstract

Human mobility generation aims to synthesize realistic point-of-interest (POI) visitation trajectories and has become an important tool for travel behavior modeling, transportation management, and urban planning. Existing diffusion-based methods achieve high fidelity but require per-city generation, given the inherent heterogeneity of geospatial locations and POI categories, while large language model-based methods generalize across cities but remain too costly at scale, especially for long-horizon trajectory generation. To address this, we propose SeMoFlow, a Semantic human Mobility generation framework based on latent Flow matching. We first encode heterogeneous POIs from different cities into a shared cross-city representation space via hierarchical Semantic IDs, where shared prefixes capture transferable semantics, and successive codes progressively refine the representation toward individual POIs. Building on the semantic IDs, SeMoFlow follows a plan-to-synthesis hierarchical generation paradigm, in which an autoregressive planner generates coarse-grained semantic and recurrence patterns, and a flow matching realizer synthesizes fine-grained suffix latents. The generated latents are subsequently decoded and grounded to concrete POIs. Extensive experiments on large-scale multi-city datasets show that SeMoFlow achieves higher trajectory fidelity than existing baselines, preserves city-specific mobility motifs, and supports both joint multi-city generation and effective cross-city transfer.

Problem

• Human Mobility Generation
• Cross-City Transfer
• Point-of-Interest Trajectory
• Trajectory Synthesis

Innovation

• Latent Flow Matching
• Semantic IDs
• Cross-City Transfer
• Human Mobility Generation
• Plan-to-Synthesis