Refining time-space traffic diagrams: A neighborhood-adaptive linear regression method

2026-03-25

AI Summary

This study addresses the low resolution commonly found in existing spatiotemporal traffic graphs, which stems from limitations in monitoring accuracy and sampling frequency and hinders both theoretical research and practical applications. To overcome this, the authors propose a refinement method based on neighborhood-adaptive linear regression. By leveraging neighborhood embedding and local pattern similarity analysis, the approach adaptively identifies similar neighboring units for each target cell and constructs a local low-to-high-resolution mapping, thereby circumventing the over-smoothing tendency of conventional global linear models. Requiring only a small amount of paired data, the method accurately captures key traffic dynamics such as wave propagation and congestion evolution. Evaluated on two real-world datasets, it achieves consistent improvements—9.16% in MAE, 8.16% in MAPE, 1.86% in CMJS, 3.89% in SSIM, and 5.83% in GMSD—and demonstrates strong generalization across days and scenarios.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The time-space (TS) traffic diagram serves as a crucial tool for characterizing the dynamic evolution of traffic flow, with its resolution directly influencing the effectiveness of traffic theory research and engineering applications. However, constrained by monitoring precision and sampling frequency, existing TS traffic diagrams commonly suffer from low resolution. To address this issue, this paper proposes a refinement method for TS traffic diagrams based on neighborhood-adaptive linear regression. Introducing the concept of neighborhood embedding into TS diagram refinement, the method leverages local pattern similarity in TS diagrams, adaptively identifies neighborhoods similar to target cells, and fits the low-to-high resolution mapping within these neighborhoods for refinement. It avoids the over-smoothing tendency of the traditional global linear model, allows the capture of unique traffic wave propagation and congestion evolution characteristics, and outperforms the traditional neighborhood embedding method in terms of local information utilization to achieve target cell refinement. Validation on two real datasets across multiple scales and upscaling factors shows that, compared to benchmark methods, the proposed method achieves improvements of 9.16%, 8.16%, 1.86%, 3.89%, and 5.83% in metrics including MAE, MAPE, CMJS, SSIM, and GMSD, respectively. Furthermore, the proposed method exhibits strong generalization and robustness in cross-day and cross-scenario validations. In summary, requiring only a minimal amount of paired high- and low-resolution training data, the proposed method features a concise formulation, providing a foundation for the low-cost, fine-grained refinement of low-sampling-rate traffic data.

Problem

• time-space traffic diagram
• low resolution
• traffic flow
• sampling frequency
• monitoring precision

Innovation

• neighborhood-adaptive linear regression
• time-space traffic diagram refinement
• local pattern similarity
• traffic wave propagation
• low-resolution traffic data enhancement