高保真合成GPS轨迹的生成在交通、城市规划及假设情景（what-if scenario）仿真等应用中日益重要，尤其在隐私问题限制真实移动数据获取的背景下。现有轨迹生成模型在效率与对路网拓扑结构的保真度之间面临权衡：连续空间方法可实现快速生成，但忽略路网结构；而拓扑感知方法则依赖基于搜索的自回归解码，导致生成速度受限。本文提出TrajDLM，一种基于分块扩散语言模型（block diffusion language model）的拓扑感知轨迹生成框架，以弥合该鸿沟。TrajDLM将轨迹建模为离散道路路段序列，融合了用于高效去噪的分块扩散主干网络、来自路网编码器的拓扑感知嵌入，以及拓扑约束采样机制，从而确保生成轨迹的连贯性与真实性。在三个城市尺度数据集上的实验表明，TrajDLM在细粒度局部相似性指标上表现优异，同时生成速度较先前工作最高提升2.8倍，并展现出强大的零样本跨域迁移能力（包括未见过的交通方式）。结果表明，分块式离散扩散是一种可扩展的、兼顾精度与效率的轨迹生成方法。代码开源地址：https://github.com/cruiseresearchgroup/TrajDLM/

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/