Traffic Control via Fleets of Connected and Automated Vehicles

In this paper, we propose three control strategies, based on different levels of cooperation (centralized, decentralized and quasi-decentralized), to improve density dependent traffic performance indexes, such as fuel consumption, by acting on a small number of Connected and Automated Vehicles (CAVs) operating as moving bottlenecks on the surrounding flow. We rely on a multi-scale approach to model mixed traffic of CAVs in the bulk flow. In particular, CAVs are individually tracked and they are allowed to overtake (if on distinct lanes) or queuing (if on the same lane). Controlling CAVs desired speeds allows to act on the system to minimize the selected cost function. For the proposed control strategies, we apply both global optimization and a Model Predictive Control approach. In particular, we perform numerical tests to investigate how the CAVs number and positions impact the result, showing that few, optimally chosen vehicles are sufficient to significantly improve the selected performance indexes, even using a decentralized control policy. Simulation results support the attractive perspective of exploiting a very small number of vehicles as endogenous control actuators to regulate traffic flow on road networks, providing a flexible alternative to traditional control methods.

I. Introduction

The recent technological advances in connectivity and automation for the automotive industry are transforming the transportation sector and impacting the related socio-economical aspects. In particular, Connected and Automated Vehicles (CAVs), which are expected to dominate the vehicle market in the next future, have raised the interest of researchers for their potential impact on traffic flow, with the aim of improving traffic conditions and safety. Several studies have shown that CAVs can be employed to control the overall traffic to mitigate congestion and improve throughput, with a consequent reduction of pollutant emissions. This has been proved by model based theoretical results [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], machine learning approaches [11], [12] and real world experiments [13]. All these investigations show that even a small number of automated vehicles among human-driven vehicles can bring benefits to the whole system, by dissipating stop-and-go waves, improving the throughput and reducing traffic flow emissions and consumption. In this perspective, CAV control can offer a valid, flexible and cheap alternative to more traditional traffic management strategies, such as ramp metering and variable speed limits [14], [15], [16], [17], [18], which require specific infrastructures.