Map matching for sparse trajectories is a fundamental problem for many trajectory-based applications, e.g., traffic scheduling and traffic flow analysis. Existing methods for map matching are generally based on Hidden Markov Model (HMM) or encoder-decoder framework. However, these methods continue to face significant challenges when handling noisy or sparsely sampled GPS trajectories. To address these limitations, we propose DiffMM, an encoder-diffusion-based map matching framework that produces effective yet efficient matching results through a one-step diffusion process. We first introduce a road segment-aware trajectory encoder that jointly embeds the input trajectory and its surrounding candidate road segments into a shared latent space through an attention mechanism. Next, we propose a one step diffusion method to realize map matching through a shortcut model by leveraging the joint embedding of the trajectory and candidate road segments as conditioning context. We conduct extensive experiments on large-scale trajectory datasets, demonstrating that our approach consistently outperforms state-of-the-art map matching methods in terms of both accuracy and efficiency, particularly for sparse trajectories and complex road network topologies.

翻译：稀疏轨迹的地图匹配是许多基于轨迹应用（如交通调度与交通流分析）的基础问题。现有地图匹配方法通常基于隐马尔可夫模型（HMM）或编码器-解码器框架。然而，这些方法在处理噪声干扰或采样稀疏的GPS轨迹时仍面临显著挑战。为突破这些局限，本文提出DiffMM——一种基于编码器-扩散机制的地图匹配框架，通过一步扩散过程实现高效且精确的匹配结果。我们首先设计了一种路段感知轨迹编码器，通过注意力机制将输入轨迹及其周边候选路段共同嵌入共享潜在空间。随后，我们提出一步扩散方法，利用轨迹与候选路段的联合嵌入作为条件上下文，通过捷径模型实现地图匹配。我们在大规模轨迹数据集上进行了广泛实验，结果表明：本方法在准确率与效率方面均持续优于当前最优的地图匹配方法，尤其适用于稀疏轨迹与复杂路网拓扑场景。