A Simple Yet Efficient K-Nearest Neighbor-Based Method for High-Resolution Traffic Time–Space Diagram Imputation

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.