TrajDiff: Diffusion Bridge Network with Semantic Alignment for Trajectory Similarity Computation (2506.15898v1)

Abstract: With the proliferation of location-tracking technologies, massive volumes of trajectory data are continuously being collected. As a fundamental task in trajectory data mining, trajectory similarity computation plays a critical role in a wide range of real-world applications. However, existing learning-based methods face three challenges: First, they ignore the semantic gap between GPS and grid features in trajectories, making it difficult to obtain meaningful trajectory embeddings. Second, the noise inherent in the trajectories, as well as the noise introduced during grid discretization, obscures the true motion patterns of the trajectories. Third, existing methods focus solely on point-wise and pair-wise losses, without utilizing the global ranking information obtained by sorting all trajectories according to their similarity to a given trajectory. To address the aforementioned challenges, we propose a novel trajectory similarity computation framework, named TrajDiff. Specifically, the semantic alignment module relies on cross-attention and an attention score mask mechanism with adaptive fusion, effectively eliminating semantic discrepancies between data at two scales and generating a unified representation. Additionally, the DDBM-based Noise-robust Pre-Training introduces the transfer patterns between any two trajectories into the model training process, enhancing the model's noise robustness. Finally, the overall ranking-aware regularization shifts the model's focus from a local to a global perspective, enabling it to capture the holistic ordering information among trajectories. Extensive experiments on three publicly available datasets show that TrajDiff consistently outperforms state-of-the-art baselines. In particular, it achieves an average HR@1 gain of 33.38% across all three evaluation metrics and datasets.

• The paper introduces a semantic alignment module that bridges fine-grained GPS and coarse grid features using cross-attention and adaptive masking.
• It employs Denoising Diffusion Bridge Models for noise robustness, preserving crucial trajectory patterns even in error-prone datasets.
• A ranking-aware regularization process integrates global trajectory ordering, significantly improving performance metrics such as HR@1 by ~50%.

TrajDiff: A Comprehensive Approach for Trajectory Similarity Computation through Alignment and Diffusion Models

In the burgeoning field of trajectory data mining, the precise computation of trajectory similarity is crucial for applications such as intelligent transportation, urban planning, and public health monitoring. This paper introduces TrajDiff, a novel framework designed to address significant challenges in trajectory similarity computation, including semantic misalignment, inherent data noise, and the neglect of global ranking information.

Research Contributions

1. Semantic Alignment Module (SAM): TrajDiff employs a dual-feature semantic alignment attention mechanism to bridge the semantic gap between the inherently fine-grained GPS features and their coarse-grained grid counterparts. By leveraging cross-attention and adaptive score masking, the semantic alignment effectively synthesizes trajectory information at both scales. This module addresses the issue of multiple points being reduced to the same grid value, thereby losing insightful trajectory data.
2. Noise Robustness through DDBM Pre-Training: The framework's robustness to noise is significantly enhanced through pre-training with Denoising Diffusion Bridge Models (DDBM). By modeling noise and learning transformations between trajectories, the system better preserves the true patterns hidden by noise, whether from data acquisition errors or grid discretization processes.
3. Overall Ranking-Aware Regularization: The incorporation of global ranking information into the learning paradigm through a ranking-aware regularization process shifts the model's emphasis from local point-wise or pair-wise similarities to a comprehensive understanding of trajectory ordering.

Experimental Results

TrajDiff was rigorously evaluated against eight key baselines on three datasets: Porto, GeoLife, and T-Drive. It consistently surpassed these baselines across various metrics, such as HR@1, HR@5, and HR@20, thanks to the intricate fusion of trajectory features and noise robustness strategies. The framework delivered an average HR@1 improvement of approximately 50% over baselines, with particular efficacy observed in noisy datasets like T-Drive.

Implications and Future Work

The methodology introduced by TrajDiff not only enhances the accuracy of trajectory similarity computations but also provides a robust framework adaptable to high-noise environments. The alignment and regularization techniques facilitate comprehensive and nuanced embeddings that can benefit related tasks such as anomaly detection and trajectory clustering.

The significant contributions in handling semantic misalignment and noise suggest promising applications across many AI-driven geospatial tasks. Future work could explore integrating additional external knowledge representations or further optimizing computational efficiency to manage larger datasets or real-time processing scenarios. Given its ability to learn adaptable and noise-resistant trajectory representations, TrajDiff sets a foundational milestone for subsequent innovations in trajectory data mining.