城市轨迹在建模城市动态及支撑各类智慧城市应用中起着关键作用。然而，隐私问题限制了大规模、高质量轨迹数据集的获取。轨迹生成通过合成逼真数据提供了一种有前景的替代方案，以缓解隐私风险。但现有方法未能显式捕捉出行模式，且仅能在单一条件下生成固定长度的轨迹。为解决这些局限，我们提出\textbf{HTP}，即分层生成（\textbf{H}ierarchical）出行模式（\textbf{T}ravel patterns），再利用大语言模型（LLMs）生成GPS点（\textbf{P}oints），而非直接生成GPS点。我们首先设计一种面向轨迹的残差量化变分自编码器（RQ-VAE），以由粗到细的方式将微观级GPS轨迹量化为紧凑的宏观级出行模式标记（token），这些标记可捕获丰富的路段空间不规则性，例如由交通状况引起的点密度变化。随后，我们将出行模式标记扩展至LLM词表中，以对齐轨迹表示与LLM输入，并采用监督微调（SFT）使LLM适配轨迹生成任务，从而支持在多种条件下生成出行模式序列。在两个真实世界数据集上的大量实验表明，HTP在生成质量上平均优于最强基线29.78\%。代码开源地址为：https://github.com/slzhou-xy/HTP。

Urban trajectories play a crucial role in modeling urban dynamics and supporting various smart city applications. However, privacy concerns restrict access to large-scale and high-quality trajectory datasets. Trajectory generation provides a promising alternative by synthesizing realistic data to mitigate privacy risks. However, existing methods fail to explicitly capture travel patterns and can only generate fixed-length trajectories under a single condition. To address these limitations, we propose \textbf{HTP}, which \textbf{H}ierarchically generates \textbf{T}ravel patterns first and then generates GPS \textbf{P}oints by using large language models (LLMs), rather than directly generating GPS points. We first design a trajectory-specific residual quantization variational autoencoder (RQ-VAE) that quantizes micro-level GPS trajectories into compact, macro-level travel pattern tokens in a coarse-to-fine manner. These tokens capture rich segment spatial irregularities, such as point density variations caused by traffic conditions. Then, we extend the LLM vocabulary with travel pattern tokens to align trajectory representations with the LLM input, and apply supervised fine-tuning (SFT) to align the LLM with the trajectory generation task, enabling generation of travel pattern sequences under various conditions. Extensive experiments on two real-world datasets show that HTP outperforms the strongest baseline by an average of 29.78\% in terms of generation quality. Our code is available at https://github.com/slzhou-xy/HTP.