# Traffic Flow Reconstruction from Limited Collected Data

arXiv: https://arxiv.org/abs/2602.11336 · HTML: https://mlsanity.com/paper/2602.11336/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Builds on (most cited first, 3 of 3)

- [A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation](https://mlsanity.com/paper/2106.03142/) (2021)
- [Physics-informed Learning for Identification and State Reconstruction of Traffic Density](https://mlsanity.com/paper/2103.13852/) (2021)
- [Learning-based State Reconstruction for a Scalar Hyperbolic PDE under noisy Lagrangian Sensing](https://mlsanity.com/paper/2011.09871/) (2020)

## Abstract (arXiv)

We propose an efficient method for reconstructing traffic density with low penetration rate of probe vehicles. Specifically, we rely on measuring only the initial and final positions of a small number of cars which are generated using microscopic dynamical systems. We then implement a machine learning algorithm from scratch to reconstruct the approximate traffic density. This approach leverages learning techniques to improve the accuracy of density reconstruction despite constraints in available data. For the sake of consistency, we will prove that, if only using data from dynamical systems, the approximate density predicted by our learned-based model converges to a well-known macroscopic traffic flow model when the number of vehicles approaches infinity.