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The emergence of automated vehicles may have significant impacts on traffic flow. While many studies suggest that fully automated vehicles can improve traffic flow by changing their macroscopic characteristics at different market penetration rates, recent studies show that partially automated vehicles (e.g., adaptive cruise control vehicles) commercially available on the market may negatively impact the traffic flow. With this in mind, existing traffic control strategies, such as ramp metering, may require further modification due to the change of macroscopic traffic flow characteristics in the presence of mixed autonomy traffic. However, the extent to which the ramp metering control algorithm will have to change in response to mixed autonomy traffic is still an open question. In this study, we first formulate the fundamental diagram analytically for mixed autonomy traffic, which is dependent on the market penetration rates of automated vehicles. Next, we model and simulate the composite traffic flow with a mixed autonomy macroscopic traffic flow model, and modify a standard ramp metering control strategy to optimize operations under the new flow conditions. The simulation results show that the total time spent is reduced by 4% compared to the scenario controlled with a standard ramp metering control strategy, suggesting a higher highway throughput is obtained at a merge section when leveraging the modified ramp metering controller. The results also show that the system-wide total delay is reduced by 11% compared to the scenario controlled with a standard ramp metering control strategy, implying the proposed ramp metering control strategy is efficient. • Traffic fundamental diagram depends on the fraction of automated vehicles. • Traffic makeup has potential to degrade traditional ramp metering control. • Ramp metering designed to consider automated vehicles in the flow. [ABSTRACT FROM AUTHOR]

Stop-and-go waves are easily caused by unstable traffic due to the collective behavior of human drivers, resulting in higher vehicle fuel consumption and emissions. In this article, we develop a general approach to synthesizing feedback controllers of autonomous vehicles (AVs) for smoothing unstable nonlinear mixed traffic flow, consisting of AVs and human-driven vehicles (HVs). In the context of traffic smoothing and stabilization, prior studies have mainly focused on analyzing string stability of specific car-following models linearized at flow equilibrium points. By contrast, we are interested to analytically synthesize appropriate feedback controllers of AVs for smoothing nonlinear mixed traffic in its general functional forms, covering a broad class of deterministic car-following models commonly seen in the literature. Specifically, by leveraging feedback control theory AVs are controlled to operate in such a way that they closely track a virtual speed profile, i.e., a subtler version of the disturbance resulting from the immediate preceding vehicle. Thus, traffic waves are reduced when propagating backward across controlled AVs. Based on the general functional form of car-following dynamics, we derive a class of effective additive AV controllers that are proven to be able to ensure convergence in tracking desired speed profiles, leading to smoother traffic. In addition, a set of sufficient conditions is devised for guaranteeing car-following safety. More importantly, head-to-tail string stability of the mixed traffic is likely to be achieved with a sufficient market penetration rate (MPR) of AVs. Notably, unlike many existing studies the feedback controllers synthesized for AVs require only local traffic information without having to rely on high degrees of vehicle connectivity, and the rate of smoothing mixed traffic is readily tunable, which is useful for practical implementations. The proposed approach is further illustrated with a theoretical intelligent driver model (IDM) and commercially available adaptive cruise control (ACC) vehicles represented by a well calibrated IDM. Extensive numerical results are presented to show the effectiveness and robustness of the feedback controllers synthesized for AVs on smoothing nonlinear mixed traffic. • Virtual tracking for autonomous vehicles to smooth nonlinear traffic • Applicable to a broad class of deterministic car-following dynamics • Only local traffic information is required for controller synthesis of autonomous vehicles [ABSTRACT FROM AUTHOR]

• Adaptive cruise control vehicles decrease bottleneck capacity. • String stability of the bulk traffic flow is positively correlated with capacity. • Commercially-available adaptive cruise control vehicles reduce highway capacity. Adaptive cruise control (ACC) vehicles are proving to be the first generation of automated vehicles. Although many studies have found that, theoretically, ACC vehicles may have positive impacts on traffic string stability and throughput, recent studies reveal that commercially-available ACC vehicles are string unstable. However, it is still not clear what impacts on highway traffic these commercially-available ACC vehicles may have at higher market penetration rates. Therefore, the objective of this study is twofold: (i) investigate the impacts on highway traffic capacity of mixed human-piloted vehicles and theoretical ACC vehicles as well as commercially-available ACC vehicles at different market penetration rates; (ii) explore the potential relationship between string stability and highway throughput. We use the intelligent driver model with calibrated parameter values from car following data collected on commercially-available ACC vehicles and compare the simulation results to simulations conducted using a theoretical ACC vehicle model with parameter values obtained in the literature. The results show that when simulating ACC vehicles found in the literature,traffic flow is string stable and the bottleneck capacity increases by up to 7% at higher market penetration rates as compared to homogeneous human driven traffic. When commercially-available ACC vehicles are simulated, the traffic flow is string unstable and the bottleneck capacity decreases at higher market penetration rates with some scenarios resulting in a decrease in capacity of up to 35%. The analysis provides evidence that bottleneck capacity is substantially affected by string stability and inter-vehicle time gap, while downstream throughput is solely influenced by time gap. [ABSTRACT FROM AUTHOR]