  This article introduces a new virtual trajectory dataset derived from the
I-24 MOTION INCEPTION v1.0.0 dataset to address challenges in analyzing large
but noisy trajectory datasets. Building on the concept of virtual trajectories,
we provide a Python implementation to generate virtual trajectories from large
raw datasets that are typically challenging to process due to their size. We
demonstrate the practical utility of these trajectories in assessing speed
variability and travel times across different lanes within the INCEPTION
dataset. The virtual trajectory dataset opens future research on traffic waves
and their impact on energy.
