  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.
