在本地差分隐私（LDP）下进行路网轨迹合成，能够在不将原始轨迹上传至不可信服务器的情况下实现移动数据分析。现有的本地私有轨迹合成方法主要对完整轨迹、点序列或网格级移动模式进行建模。然而，这些方法无法处理由局部路网和轨迹上下文（如端点、长停留关联区域和低出度分支）引起的非均匀隐私风险。此外，它们也未直接保护路网上的条件下一跳选择，因为用户选择的后继节点可能泄露敏感的局部移动意图。为应对这些挑战，我们提出了 TrajRACE，一种针对路网轨迹的本地私有合成框架。TrajRACE 使用起点报告、分段计数报告和转移报告来表示轨迹。对于每次转移，它将当前路段作为公开聚合上下文，并在客户端侧对合法后继中的真实后继进行扰动。随后，根据局部路网和轨迹上下文为各次转移分配风险感知的保护强度。最后，服务器从异构的私有化报告中恢复群体级的起点、分段计数和条件转移统计信息，并生成遵循公开路网拓扑结构的合成轨迹。在三个路网轨迹数据集上的实验表明，TrajRACE 在分布性、查询式和模式级指标上均提升了合成数据的效用，同时为高风险局部转移提供了更强的保护。

Road-network trajectory synthesis under local differential privacy (LDP) enables mobility data analysis without uploading raw trajectories to an untrusted server. Existing locally private trajectory synthesis methods mainly model whole trajectories, point sequences, or grid-level mobility patterns. However, they cannot handle non-uniform privacy risks caused by local road-network and trajectory contexts, such as endpoints, long-stay-associated regions, and low-out-degree branches. They also do not directly protect conditional next-hop choices on road networks, where a user's selected successor may reveal sensitive local movement intentions. To address these challenges, we propose TrajRACE, a locally private synthesis framework for road-network trajectories. TrajRACE uses start, segment-count, and transition reports to represent trajectories. For each transition, it keeps the current road segment as a public aggregation context and perturbs the true successor among legal successors on the client side. It then assigns risk-aware protection strengths to transitions according to local road-network and trajectory contexts. Finally, the server recovers population-level start, segment-count, and conditional transition statistics from heterogeneous privatized reports, and generates synthetic trajectories that follow the public road-network topology. Experiments on three road-network trajectory datasets demonstrate the effectiveness of TrajRACE in improving synthetic-data utility across distributional, query-based, and pattern-level metrics, while providing stronger protection for high-risk local transitions.