Synthetic vehicle speed trajectory generation is essential for evaluating vehicle control algorithms and connected vehicle technologies. Traditional Markov chain approaches suffer from discretization artifacts and limited expressiveness. This paper proposes a physics-informed diffusion framework for conditional micro-trip synthesis, combining a dual-channel speed-acceleration representation with soft physics constraints that resolve optimization conflicts inherent to hard-constraint formulations. We compare a 1D U-Net architecture against a transformer-based Conditional Score-based Diffusion Imputation (CSDI) model using 6,367 GPS-derived micro-trips. CSDI achieves superior distribution matching (Wasserstein distance 0.30 for speed, 0.026 for acceleration), strong indistinguishability from real data (discriminative score 0.49), and validated utility for downstream energy assessment tasks. The methodology enables scalable generation of realistic driving profiles for intelligent transportation systems (ITS) applications without costly field data collection.

翻译：合成车辆速度轨迹生成对于评估车辆控制算法与网联车辆技术至关重要。传统马尔可夫链方法存在离散化伪影和表达能力有限的问题。本文提出一种用于条件微行程合成的物理信息扩散框架，该框架结合了双通道速度-加速度表示与软物理约束，解决了硬约束公式固有的优化冲突。我们使用6,367条GPS衍生的微行程数据，比较了一维U-Net架构与基于Transformer的条件分数扩散插补（CSDI）模型。CSDI实现了优异的分布匹配（速度Wasserstein距离0.30，加速度0.026）、与真实数据的强不可区分性（判别分数0.49），并验证了下游能量评估任务的应用效用。该方法能够为智能交通系统（ITS）应用可扩展地生成真实驾驶剖面，无需昂贵的实地数据收集。