Virtual trajectories for I-24 MOTION: data and tools

TL;DR

This paper presents a new virtual trajectory dataset derived from the I-24 MOTION dataset, along with Python tools to generate and analyze these trajectories for traffic research.

Contribution

It introduces a novel virtual trajectory dataset and a Python implementation to generate and analyze trajectories from large raw datasets, facilitating traffic flow studies.

Findings

Virtual trajectories help assess speed variability.

They enable analysis of travel times across lanes.

The dataset supports future research on traffic waves.

Abstract

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.