TrajDLM: Topology-Aware Block Diffusion Language Model for Trajectory Generation

• URL: http://arxiv.org/abs/2605.10020v1
• Date: Mon, 11 May 2026 05:44:40 GMT
• Title: TrajDLM: Topology-Aware Block Diffusion Language Model for Trajectory Generation
• Abstract summary: TrajDLM is a topology-aware trajectory generation framework based on block diffusion language models.<n>Across three city-scale datasets, TrajDLM achieves strong performance on fine-grained local similarity metrics.<n>Results highlight the effectiveness of block-wise discrete diffusion as a scalable approach to accurate and efficient trajectory generation.
• Score: 12.444424009219077
• License: http://creativecommons.org/licenses/by/4.0/
• 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 https://github.com/cruiseresearchgroup/TrajDLM/

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