ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model
This addresses privacy and cost issues in mobility analysis by improving trajectory generation, though it appears incremental as it builds on existing diffusion models with added constraints.
The authors tackled the problem of generating realistic human mobility trajectories by proposing ControlTraj, a framework that integrates road network topology constraints into a diffusion model, resulting in high-fidelity and adaptable trajectory generation across three real-world datasets.
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.