AGC-ODE: Adaptive Graph Controlled Neural ODE for Human Mobility Prediction

Despite the substantial progress in predicting human mobility, most existing methods fail to reveal the spatiotemporal patterns under significant interventions such as COVID-19, which disrupt the routine of human mobility. To fill this gap, this paper presents a unified framework for learning human mobility in both regular and intervened scenarios through explicit modeling of the intervention and the intervened system. To be concrete, the authors design a novel Deep State-Space Model (DSSM) called AGC-ODE: Adaptive Graph Controlled Neural Ordinary Differential Equation for human mobility prediction during COVID-19. The transition equation that describes continuous-time dynamics of human mobility is parameterized with a graph-controlled Neural ODE, and the latent control that guides the equation propagating is inferred through the multi-head gating filters. Additionally, an information capacity constraint is applied to foster the disentanglement of interventions. Lastly, AGC-ODE utilizes a data-driven initialization strategy to improve DSSM’s initial state estimation. They conduct extensive experiments and analysis on two real-world datasets of Beijing and the U.S. to demonstrate the superiority and interpretability of their model. Furthermore, they introduce a deployed system that is based on AGC-ODE and how it helps epidemic prevention during the COVID era and work resumption in the post-COVID era.

• Record URL:
  • https://doi.org/10.1109/TITS.2024.3447161
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2024, IEEE.
• Authors:
  • Xiang, Yinfeng
  • Li, Chao
  • He, Shibo
  • Chen, Jiming
• Publication Date: 2024-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 18449-18460
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 25
  • Issue Number: 11
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: COVID-19; Graphs; Mobility; Neural networks; Travel patterns
• Geographic Terms: Beijing (China); United States
• Subject Areas: Data and Information Technology; Safety and Human Factors; Security and Emergencies; Transportation (General);

Filing Info

• Accession Number: 01948492
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 14 2025 4:28PM