A Control-Oriented Highway Traffic Model with Multiple Clusters of CAVs

The presented work falls in the field of modelling the presence of multiple clusters of connected autonomous vehicles (CAVs), i.e., groups of CAVs in traffic flow that, if properly controlled, can positively influence traffic behavior by acting as actuators of specific control strategies. An extended version of the well-known Cell Transmission Model (CTM) is used as a traffic model, where the cells of a highway stretch are divided into two types: cells with clusters of CAVs and cells without. The classical CTM is used to model the second type of cells, while for the first type we model the presence of a cluster as a moving bottleneck that divides the cell into different parts. The proposed control-oriented traffic model has been tested on a case study based on real traffic data.

I. Introduction

Reaching fully automated vehicles is today closer than ever thanks to all the technological advancements and is considered as a promising solution to a great number of problems. In the near future, traffic will be a mix of human-driven vehicles (HVs) and CAVs and even with small penetration rates, CAVs will bring a lot of benefits according to some studies [1], thanks to their computer sensors onboard and their capability of communicating with other CAVs and with the infrastructure. As both types of vehicles have different driving performances and behavior, interacting with each other on the road may create scenarios and problems not seen before in conventional traffic composed only of HVs. To address this, many researchers focused on modeling the mixed traffic flow using physics-based models [2]. Such models have low number of parameters to calibrate and can represent car following and lane changing behaviour, especially in mixed traffic of HVs and CAVs [3] [4].