Abstract:Generating high-fidelity synthetic GPS trajectories is increasingly important for applications in transportation, urban planning, and what-if scenario simulation, especially as privacy concerns limit access to real-world mobility data. Existing trajectory generation models face a trade-off between efficiency and faithfulness to road network topology: continuous-space methods enable fast generation but ignore the road network, while topology-aware approaches rely on search-based autoregressive decoding that limits generation speed. We propose TrajDLM, a topology-aware trajectory generation framework based on block diffusion language models that bridges this gap. TrajDLM models trajectories as sequences of discrete road segments, combining a block diffusion backbone for efficient denoising, topology-aware embeddings from a road network encoder, and topology-constrained sampling to ensure coherent and realistic trajectories. Across three city-scale datasets, TrajDLM achieves strong performance on fine-grained local similarity metrics while being up to $2.8\times$ faster than prior work, and demonstrates strong zero-shot transfer across domains, including unseen transportation modes. These results highlight the effectiveness of block-wise discrete diffusion as a scalable approach to accurate and efficient trajectory generation. Our code is available at this https URL
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.10020 [cs.LG] |
| (or arXiv:2605.10020v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.10020 arXiv-issued DOI via DataCite (pending registration) |
From: Wilson Wongso [view email]
[v1]
Mon, 11 May 2026 05:44:40 UTC (10,364 KB)
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