# Virtual trajectories for I-24 MOTION: data and tools

arXiv: https://arxiv.org/abs/2311.10888 · HTML: https://mlsanity.com/paper/2311.10888/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2023.

## Built on by (most cited first, 3 of 3)

- [Discovering the Precursors of Traffic Breakdowns Using Spatiotemporal Graph Attribution Networks](https://mlsanity.com/paper/2504.17109/) (2025)
- [Calibrating Adaptive Smoothing Methods for Freeway Traffic Reconstruction](https://mlsanity.com/paper/2602.02072/) (2026)
- [Refining time-space traffic diagrams: A neighborhood-adaptive linear regression method](https://mlsanity.com/paper/2603.24312/) (2026)

## Abstract (arXiv)

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.