DiffMM: Efficient Method for Accurate Noisy and Sparse Trajectory Map Matching via One Step Diffusion

• URL: http://arxiv.org/abs/2601.08482v1
• Date: Tue, 13 Jan 2026 12:14:57 GMT
• Title: DiffMM: Efficient Method for Accurate Noisy and Sparse Trajectory Map Matching via One Step Diffusion
• Authors: Chenxu Han, Sean Bin Yang, Jilin Hu,
• Abstract summary: We propose DiffMM, an encoder-diffusion-based map matching framework.<n>We conduct extensive experiments on large-scale trajectory datasets.<n>Our approach consistently outperforms state-of-the-art map matching methods in terms of both accuracy and efficiency.
• Score: 7.910040077410762
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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