Traffic Flow Reconstruction from Limited Collected Data

TL;DR

This paper introduces a machine learning-based method to reconstruct traffic density using limited data, specifically initial and final positions of a few vehicles, and proves its convergence to established traffic flow models.

Contribution

The paper presents a novel machine learning approach for traffic density reconstruction from minimal data and demonstrates its theoretical convergence to macroscopic traffic models.

Findings

Reconstruction accuracy improves with increased vehicle data.

The learned model converges to classical traffic flow models as vehicle number grows.

Method effectively reconstructs traffic density with limited probe vehicle data.

Abstract

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.