  Automated Vehicles (AVs) promise significant advances in transportation.
Critical to these improvements is understanding AVs' longitudinal behavior,
relying heavily on real-world trajectory data. Existing open-source trajectory
datasets of AV, however, often fall short in refinement, reliability, and
completeness, hindering effective performance metrics analysis and model
development. This study addresses these challenges by creating a Unified
Longitudinal TRAjectory dataset for AVs (Ultra-AV) to analyze their microscopic
longitudinal driving behaviors. This dataset compiles data from 13 distinct
sources, encompassing various AV types, test sites, and experiment scenarios.
We established a three-step data processing: 1. extraction of longitudinal
trajectory data, 2. general data cleaning, and 3. data-specific cleaning to
obtain the longitudinal trajectory data and car-following trajectory data. The
validity of the processed data is affirmed through performance evaluations
across safety, mobility, stability, and sustainability, along with an analysis
of the relationships between variables in car-following models. Our work not
only furnishes researchers with standardized data and metrics for longitudinal
AV behavior studies but also sets guidelines for data collection and model
development.
