Virtual trajectories for I-24 MOTION: data and tools

• DOI: 10.48550/arxiv.2311.10888
• Publication Date: 2024-02-26
• Authors: Ji, Junyi; Wang, Yanbing; Gloudemans, Derek; Zachár, Gergely; Barbour, William; Work, Daniel B.
• Keywords: Physics - Data Analysis, Statistics and Probability; Image and Video Processing (eess.IV); FOS: Electrical engineering, electronic engineering, information engineering; FOS: Physical sciences; Electrical Engineering and Systems Science - Image and Video Processing; Data Analysis, Statistics and Probability (physics.data-an)

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.