ActivityEditor: Learning to Synthesize Physically Valid Human Mobility
Summary: arXiv:2604.05529v1 Announce Type: new
Abstract: Human mobility modeling is indispensable for diverse urban applications. However, existing data-driven methods often suffer from data scarcity, limiting their applicability in regions where historical trajectories are unavailable or restricted. To bridge this gap, we propose ActivityEditor, a novel dual-LLM-agent framework designed for zero-shot cross-regional trajectory generation.
The Challenges of Human Mobility Modeling
Human mobility modeling plays a crucial role in understanding urban dynamics, predicting traffic patterns, and enhancing smart city planning. Despite its importance, conventional methods often rely heavily on historical trajectory data, which may not be available in all urban settings. This presents a significant challenge in regions lacking comprehensive mobility data.
Introducing ActivityEditor
The ActivityEditor framework addresses these limitations by introducing a two-stage collaborative approach involving dual agents. This innovative model is designed to generate human mobility trajectories in areas where data is scarce.
Framework Components
- Intention-Based Agent: This agent utilizes demographic-driven priors to create structured human intentions and coarse activity chains. By focusing on high-level socio-semantic coherence, it ensures that the generated activities are relevant and contextually appropriate.
- Editor Agent: Following the initial generation of intentions, the editor agent refines these outputs into complete mobility trajectories. This refinement process involves iterative revisions that enforce human mobility laws, ensuring that the final results adhere to real-world physical constraints.
Reinforcement Learning for Mobility Synthesis
One of the key innovations of ActivityEditor is its use of reinforcement learning to enhance the trajectory generation process. The framework employs various reward mechanisms grounded in physical constraints to help the agent internalize mobility regularities. This enables the generation of high-fidelity trajectories that not only meet statistical standards but also conform to the laws of human mobility.
Performance and Applicability
Extensive experiments have shown that ActivityEditor outperforms existing methods, particularly in zero-shot scenarios where it is applied across different urban contexts. The results indicate that the framework maintains high statistical fidelity while ensuring physical validity, making it a robust tool for mobility simulation.
Conclusion
The implications of ActivityEditor span various urban applications, offering a solution for cities struggling with data scarcity in mobility modeling. As urban environments continue to evolve, frameworks like ActivityEditor will be essential for accurate mobility simulation and urban planning.
For those interested in exploring the framework further, the code is available at: ActivityEditor Code Repository.