ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model

TL;DR

ControlTraj introduces a novel diffusion-based framework that generates high-fidelity, controllable human mobility trajectories by incorporating road network topology constraints, addressing privacy and data collection challenges.

Contribution

It presents a new topology-constrained diffusion model with a road segment autoencoder and GeoUNet architecture for realistic trajectory synthesis.

Findings

Produces high-fidelity trajectories with structural constraints.

Demonstrates adaptability across diverse geographical contexts.

Outperforms existing methods in realism and controllability.

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.