This paper presents a novel approach to improve traffic throughput near diverge and weave bottlenecks in mixed traffic with human-driven vehicles (HDVs) and connected automated vehicles (CAVs). This is done by the strategic assignment of CAVs across lanes. The main principle is to induce strategic and necessary lane changes (LCs) (by CAVs and HDVs) well upstream of the potential bottleneck, so that the traffic flow approaching the bottleneck is organized and exhibits fewer throughput-reducing LCs at the bottleneck. A hybrid approach is used to investigate the problem: macroscopic analytical approach to formulate lane assignment strategies, and numerical simulations to quantify the improvements in throughput for various scenarios. Several strategies are formulated considering various operational conditions for each bottleneck type. Furthermore, compensatory behaviour of HDVs in response to the flow/density imbalance created by the CAV lane assignment is explicitly accounted for in our framework. Evaluation by numerical simulations demonstrates significant benefits of the proposed method, even at low to moderate CAV penetration rates: they can lead to an increase of throughput by several percent, thereby decreasing delays significantly.

This paper presents a car-following control strategy of connected automated vehicles (CAVs) to stabilize a mixed vehicular platoon consisting of CAVs and human-driven vehicles. This study first establishes a string stability criterion for a mixed vehicular platoon. Specifically, a mixed vehicular platoon is decomposed into “subsystems” that are all possible sequential subsets of the platoon. String stability is then defined as the “head-to-tail” string stability for all subsystems: the magnitude of a disturbance is not amplified from the first vehicle to the last vehicle of each subsystem. Based on this definition, distributed frequency-domain-based CAV control is proposed to increase the number of head-to-tail string stable subsystems and consequently dampen stop-and-go disturbances drastically. Specifically, an H-infinity control problem is formulated, where the maximum disturbance “damping ratios” in each subsystem is minimized within the predominant acceleration frequency boundaries of human-driven vehicles. Simulation experiments, embedded with real human-driven vehicle trajectories, were conducted, and results show that the proposed control can effectively dampen stop-and-go disturbances.

In this paper, a serial distributed model predictive control (MPC) approach for connected automated vehicles (CAVS) is developed with local stability (disturbance dissipation over time) and multi-criteria string stability (disturbance attenuation through a vehicular string). Two string stability criteria are considered within the proposed MPC: (i) the l_∞-norm string stability criterion for attenuation of the maximum disturbance magnitude and (ii) l₂-norm string stability criterion for attenuation of disturbance energy. The l_∞-norm string stability is achieved by formulating constraints within the MPC based on the future states of the leading CAV, and the l₂-norm string stability is achieved by proper weight matrix tuning over a robust positive invariant set. For rigor, mathematical proofs for asymptotical local stability and multi-criteria string stability are provided. Simulation experiments verify that the distributed serial MPC proposed in this study is effective for disturbance attenuation and performs better than traditional MPC without stability guarantee.