Abstract
A time–space diagram (TSD) is an efficient tool for traffic analysis and visualization, representing the macroscopic traffic state as a set of cells. However, its application is often hampered by data sparsity, which obscures high-resolution traffic dynamics. This study proposes a modified K-nearest neighbors method, characterized by an adaptive iterative process, to impute missing TSD data. To support the method’s design, analytical bounds on error propagation motivated by Green’s function-based theory are established, and a practical empirical formula for the optimal K parameter is derived. The framework’s performance was rigorously validated on diverse data sets from China (Ubiquitous Traffic Eyes), the US (Next Generation Simulation), and Germany (HighD) across 30 distinct experimental conditions. Compared against four baseline models, the proposed model demonstrates a compelling balance between high imputation accuracy and exceptional computational efficiency. Further analyses confirm the influence of neighborhood order and the systematic performance bias. The model’s potential for knowledge transfer is also demonstrated via a cross-data set imputation scheme.
Keywords
Get full access to this article
View all access options for this article.
References
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
