ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model
<|reference_start|>ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model: 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.<|reference_end|>
@article{zhu2024controltraj:,
    title={ControlTraj: Controllable Trajectory Generation with
  Topology-Constrained Diffusion Model},
    author={Yuanshao Zhu, James Jianqiao Yu, Xiangyu Zhao, Qidong Liu, Yongchao
  Ye, Wei Chen, Zijian Zhang, Xuetao Wei, Yuxuan Liang},
    journal={arXiv preprint arXiv:2404.15380},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15380},
    primaryClass={cs.LG cs.AI}
}