Dynamic Population Distribution Aware Human Trajectory Generation with Diffusion Model

2025-11-02

AI Summary

Existing trajectory generation methods largely neglect the constraining effect of dynamic population distribution on individual mobility, leading to simulation inaccuracies. To address this, we propose the first diffusion-based framework explicitly incorporating dynamic population distribution constraints: (1) a spatial graph is constructed to model trajectory spatial correlations; (2) a population-density-aware denoising network is designed to explicitly couple human mobility with spatiotemporal environmental dynamics. Evaluated on multiple real-world urban datasets, our method significantly improves trajectory fidelity—key statistical metrics—including stay distribution, transition patterns, and population density alignment—achieve average absolute errors reduced by over 54% compared to ground truth, outperforming current state-of-the-art approaches. The framework enables high-fidelity urban simulation and holds substantial practical value for transportation planning and public policy evaluation.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Diffusion Models for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Human trajectory data is crucial in urban planning, traffic engineering, and public health. However, directly using real-world trajectory data often faces challenges such as privacy concerns, data acquisition costs, and data quality. A practical solution to these challenges is trajectory generation, a method developed to simulate human mobility behaviors. Existing trajectory generation methods mainly focus on capturing individual movement patterns but often overlook the influence of population distribution on trajectory generation. In reality, dynamic population distribution reflects changes in population density across different regions, significantly impacting individual mobility behavior. Thus, we propose a novel trajectory generation framework based on a diffusion model, which integrates the dynamic population distribution constraints to guide high-fidelity generation outcomes. Specifically, we construct a spatial graph to enhance the spatial correlation of trajectories. Then, we design a dynamic population distribution aware denoising network to capture the spatiotemporal dependencies of human mobility behavior as well as the impact of population distribution in the denoising process. Extensive experiments show that the trajectories generated by our model can resemble real-world trajectories in terms of some critical statistical metrics, outperforming state-of-the-art algorithms by over 54%.

Problem

• Generating realistic human trajectories while addressing privacy and data quality concerns
• Incorporating dynamic population distribution constraints into trajectory generation models
• Capturing spatiotemporal dependencies of mobility behavior using diffusion models

Innovation

• Uses diffusion model for trajectory generation
• Integrates dynamic population distribution constraints
• Employs spatial graph to enhance trajectory correlation