A method for long car-following pair extraction and comprehensive data quality assessment: a case study using Zen Traffic Data.

This paper introduces a car-following (CF) extraction algorithm to address challenges in aerial-based trajectory data extraction. The algorithm, comprising four steps – vehicle grouping, elimination of false overtaking behavior, vehicle sorting, and CF pair matching – was applied to Zen Traffic Data, extracting 246 CF pairs. Three datasets were then generated: kilopost-based, geography-based, and velocity-based. A quality analysis revealed significant inconsistencies between data fields, with the geography-based dataset being least affected by high-frequency noise. The extracted CF data also demonstrated a more comprehensive driving regime than NGSIM, with complete driving regimes identified. Furthermore, the impact of data noise on CF model calibration and heterogeneity analysis was thoroughly assessed. This study enhances our understanding of trajectory data quality and highlights the richness of driving behavior information in Zen Traffic Data. [ABSTRACT FROM AUTHOR]