Refining Time-Space Traffic Diagrams: A Neighborhood-Adaptive Linear Regression Method

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 Mean
Absolute Error (MAE), Mean Absolute Percentage Error
(MAPE), Congestion Matrix Jaccard Similarity Coefficient
(CMJS), Structural Similarity Index Measure (SSIM), and
Gradient Magnitude Similarity Deviation (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.