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TrajFlow: Nation-wide Pseudo GPS Trajectory Generation with Flow Matching Models

Peiran Li, Jiawei Wang, Haoran Zhang, Xiaodan Shi, Noboru Koshizuka, Chihiro Shimizu, Renhe Jiang

Abstract

The importance of mobile phone GPS trajectory data is widely recognized across many fields, yet the use of real data is often hindered by privacy concerns, limited accessibility, and high acquisition costs. As a result, generating pseudo-GPS trajectory data has become an active area of research. Recent diffusion-based approaches have achieved strong fidelity but remain limited in spatial scale (small urban areas), transportation-mode diversity, and efficiency (requiring numerous sampling steps). To address these challenges, we introduce TrajFlow, which to the best of our knowledge is the first flow-matching-based generative model for GPS trajectory generation. TrajFlow leverages the flow-matching paradigm to improve robustness and efficiency across multiple geospatial scales, and incorporates a trajectory harmonization and reconstruction strategy to jointly address scalability, diversity, and efficiency. Using a nationwide mobile phone GPS dataset with millions of trajectories across Japan, we show that TrajFlow or its variants consistently outperform diffusion-based and deep generative baselines at urban, metropolitan, and nationwide levels. As the first nationwide, multi-scale GPS trajectory generation model, TrajFlow demonstrates strong potential to support inter-region urban planning, traffic management, and disaster response, thereby advancing the resilience and intelligence of future mobility systems.

TrajFlow: Nation-wide Pseudo GPS Trajectory Generation with Flow Matching Models

Abstract

The importance of mobile phone GPS trajectory data is widely recognized across many fields, yet the use of real data is often hindered by privacy concerns, limited accessibility, and high acquisition costs. As a result, generating pseudo-GPS trajectory data has become an active area of research. Recent diffusion-based approaches have achieved strong fidelity but remain limited in spatial scale (small urban areas), transportation-mode diversity, and efficiency (requiring numerous sampling steps). To address these challenges, we introduce TrajFlow, which to the best of our knowledge is the first flow-matching-based generative model for GPS trajectory generation. TrajFlow leverages the flow-matching paradigm to improve robustness and efficiency across multiple geospatial scales, and incorporates a trajectory harmonization and reconstruction strategy to jointly address scalability, diversity, and efficiency. Using a nationwide mobile phone GPS dataset with millions of trajectories across Japan, we show that TrajFlow or its variants consistently outperform diffusion-based and deep generative baselines at urban, metropolitan, and nationwide levels. As the first nationwide, multi-scale GPS trajectory generation model, TrajFlow demonstrates strong potential to support inter-region urban planning, traffic management, and disaster response, thereby advancing the resilience and intelligence of future mobility systems.
Paper Structure (42 sections, 7 equations, 12 figures, 6 tables, 1 algorithm)

This paper contains 42 sections, 7 equations, 12 figures, 6 tables, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. Human Mobility Generation.
  4. GPS trajectory generation.
  5. PRELIMINARY
  6. Problem Definition
  7. Flow Matching Model
  8. TrajFlow
  9. Motivation
  10. Multi-scale GPS Trajectory Harmonization and Reconstruction
  11. GPS Trajectory Modeling Architecture
  12. Flow-matching Training and Inference
  13. EXPERIMENTS
  14. Settings
  15. Evaluation
  16. ...and 27 more sections

Figures (12)

  • Figure 1: In pseudo-human mobility generation, three key challenges remain to be addressed: multi-scale capability, transportation-mode diversity, and training & inference efficiency.
  • Figure 2: (a) shows the accuracy degradation of DiffTraj at increasing spatial scales. (b) shows the SNR collapses when applying a fixed noising parameter across scales.
  • Figure 3: The overview of the proposed TrajFlow.
  • Figure 4: Visualization of trajectory samples. Ground-truth and generated nationwide trajectories are shown with zoomed views highlighting three representative regions: (A) Tokyo Metropolis, (B) Tohoku Area, and (C) Kansai Area, across all generative models.
  • Figure 5: Per-mode average trip distance in Tokyo: ground truth vs. generated.
  • ...and 7 more figures

Theorems & Definitions (2)

  • Definition 1: Human Mobility (GPS Trajectory)
  • Definition 2: GPS Trajectory Generation