ControlTraj : Controllable Trajectory Generation with Topology-Constrained Diffusion Model
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | KDD '24 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining |
Place of Publication | New York, NY |
Publisher | Association for Computing Machinery |
Pages | 4676-4687 |
ISBN (print) | 9798400704901 |
Publication status | Published - Aug 2024 |
Publication series
Name | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
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ISSN (Print) | 2154-817X |
Conference
Title | 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024) |
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Location | Centre de Convencions Internacional de Barcelona |
Place | Spain |
City | Barcelona |
Period | 25 - 29 August 2024 |
Link(s)
Abstract
Generating trajectory data is among promising solutions to addressing privacy concerns, collection costs, and proprietary restrictions usually associated with human mobility analyses. However, existing trajectory generation methods are still in their infancy due to the inherent diversity and unpredictability of human activities, grappling with issues such as fidelity, flexibility, and generalizability. To overcome these obstacles, we propose ControlTraj, a Controllable Trajectory generation framework with the topology-constrained diffusion model. Distinct from prior approaches, ControlTraj utilizes a diffusion model to generate high-fidelity trajectories while integrating the structural constraints of road network topology to guide the geographical outcomes. Specifically, we develop a novel road segment autoencoder to extract fine-grained road segment embedding. The encoded features, along with trip attributes, are subsequently merged into the proposed geographic denoising UNet architecture, named GeoUNet, to synthesize geographic trajectories from white noise. Through experimentation across three real-world data settings, ControlTraj demonstrates its ability to produce human-directed, high-fidelity trajectory generation with adaptability to unexplored geographical contexts. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Research Area(s)
- diffusion model, gps trajectory, urban computing
Bibliographic Note
Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
Citation Format(s)
ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model. / Zhu, Yuanshao; Yu, James Jianqiao; Zhao, Xiangyu et al.
KDD '24 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY: Association for Computing Machinery, 2024. p. 4676-4687 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).
KDD '24 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY: Association for Computing Machinery, 2024. p. 4676-4687 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review