TRACE: Trajectory Recovery with State Propagation Diffusion for Urban Mobility
arXiv:2603.19474v1 Announce Type: new Abstract: High-quality GPS trajectories are essential for location-based web services and smart city applications, including navigation, ride-sharing and delivery. However, due to low sampling rates and limited infrastructure coverage during data collection, real-world trajectories are often sparse and feature unevenly distributed location points. Recovering these trajectories into dense and continuous forms is essential but challenging, given their complex and irregular spatio-temporal patterns. In this paper, we introduce a novel diffusion model for trajectory recovery named TRACE, which reconstruct dense and continuous trajectories from sparse and incomplete inputs. At the core of TRACE, we propose a State Propagation Diffusion Model (SPDM), which integrates a novel memory mechanism, so that during the denoising process, TRACE can retain and leverage intermediate results from previous steps to effectively reconstruct those hard-to-recover traje
arXiv:2603.19474v1 Announce Type: new Abstract: High-quality GPS trajectories are essential for location-based web services and smart city applications, including navigation, ride-sharing and delivery. However, due to low sampling rates and limited infrastructure coverage during data collection, real-world trajectories are often sparse and feature unevenly distributed location points. Recovering these trajectories into dense and continuous forms is essential but challenging, given their complex and irregular spatio-temporal patterns. In this paper, we introduce a novel diffusion model for trajectory recovery named TRACE, which reconstruct dense and continuous trajectories from sparse and incomplete inputs. At the core of TRACE, we propose a State Propagation Diffusion Model (SPDM), which integrates a novel memory mechanism, so that during the denoising process, TRACE can retain and leverage intermediate results from previous steps to effectively reconstruct those hard-to-recover trajectory segments. Extensive experiments on multiple real-world datasets show that TRACE outperforms the state-of-the-art, offering $>$26\% accuracy improvement without significant inference overhead. Our work strengthens the foundation for mobile and web-connected location services, advancing the quality and fairness of data-driven urban applications. Code is available at: https://github.com/JinmingWang/TRACE
Executive Summary
This article introduces TRACE, a novel diffusion model for trajectory recovery in urban mobility. TRACE reconstructs dense and continuous trajectories from sparse and incomplete GPS data using a State Propagation Diffusion Model (SPDM) that leverages intermediate results to effectively recover hard-to-recover segments. Extensive experiments show TRACE outperforms the state-of-the-art with a $>$26% accuracy improvement without significant inference overhead. This work has significant implications for location-based web services and smart city applications, including navigation, ride-sharing, and delivery. The code is available on GitHub.
Key Points
- ▸ TRACE is a novel diffusion model for trajectory recovery in urban mobility
- ▸ State Propagation Diffusion Model (SPDM) leverages intermediate results for effective recovery
- ▸ TRACE outperforms the state-of-the-art with a $>$26% accuracy improvement
Merits
Improvement in Trajectory Recovery Accuracy
TRACE achieves a $>$26% accuracy improvement over the state-of-the-art, highlighting its effectiveness in reconstructing dense and continuous trajectories from sparse and incomplete GPS data.
Efficient Inference Overhead
TRACE's architecture allows for efficient inference overhead, making it suitable for real-world applications with limited computational resources.
Demerits
Limited Evaluation on Real-World Datasets
While TRACE is evaluated on multiple real-world datasets, it would be beneficial to include more diverse and challenging datasets to further assess its robustness and generalizability.
Lack of Comparative Analysis with Other Methods
A more comprehensive comparison with other state-of-the-art methods would help to better understand the strengths and weaknesses of TRACE and its potential applications.
Expert Commentary
The introduction of TRACE represents a significant advancement in the field of urban mobility and location-based services. By leveraging a novel State Propagation Diffusion Model (SPDM) to effectively recover hard-to-recover trajectory segments, TRACE offers a substantial improvement in accuracy over the state-of-the-art methods. Its efficient inference overhead and suitability for real-world applications make it an attractive solution for various industries. However, further evaluation on diverse datasets and a more comprehensive comparison with other methods would be beneficial to fully understand the capabilities and limitations of TRACE. As the field continues to evolve, it is likely that TRACE will play a crucial role in enhancing the quality and fairness of data-driven urban applications.
Recommendations
- ✓ Future research should focus on evaluating TRACE on diverse and challenging datasets to assess its robustness and generalizability.
- ✓ A more comprehensive comparison with other state-of-the-art methods would help to better understand the strengths and weaknesses of TRACE and its potential applications.
Sources
Original: arXiv - cs.LG
