Traveller: Travel-pattern aware trajectory generation via autoregressive diffusion models

Trajectory Generation (TG) enables realistic simulation of individual movements for applications such as urban management, transportation planning, epidemic control, and privacy-preserving mobility analysis. However, existing TG methods, particularly unconditional diffusion models, struggle with spatiotemporal fidelity as they often overlook some travel patterns that are critical in an individual’s mobility behavior, such as recurrent location visits, movement scope, and temporal regularities. In this work, we propose the Autoregressive Diffusion Model for Travel-Pattern Aware Trajectory Generation (Traveller), a novel approach that integrates autoregressive travel-pattern modeling (AR-TempPlan) with diffusion-based trajectory generation (TravCond-Diff) to produce realistic and context-aware movement patterns. By leveraging the spatial anchor and temporal modes of visiting different locations, we derive an individual’s particular travel pattern as spatiotemporal constraints for guided trajectory generation. Building on this, AR-TempPlan generates a mask location sequence as the temporal modes, planning location transitions over time, while TravCond-Diff leverages this planning signal and home location, the spatial anchor, to guide spatial generation through a discrete diffusion process. Experiments on real-world datasets demonstrate that Traveller with the dual guidance mechanism enables the production of high-fidelity and individual trajectories that effectively capture complex human mobility behaviors while preserving privacy. The code and data are available at https://github.com/YuxiaoLuo0013/Traveller.