New frontiers of freeway traffic control and estimation

This article provides an overview of the classical and new techniques in traffic flow control and estimations. The overview begins with a description of the most used traffic flow models for estimation and control. Then, it shifts towards using those models for traffic flow estimation using physics-informed machine learning techniques. Lastly, it focuses on traffic flow control describing the most classical techniques and the new advancement in traffic control using autonomous vehicles.

I. Introduction

This paper is an overview of the state of the art of traffic control and estimation both for classical techniques and for newer methods. The overviews start with a brief description of the most common models used for traffic control and estimation, looking towards models that include human and automated traffic. We will focus on the challenges that each model presents when dealing with traffic flow control and estimation problems. We then analyze how traffic flow is reconstructed using the most novel techniques as physics informed machine learning. The second part of the paper is focused on control techniques for freeway traffic flow, starting from the most classical ones, to end with the newest techniques that make use of connected and automated vehicles.