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• A virtual risk computation based on matching degree was introduced. • A universal planning method for pre-merging in mixed traffic and regular car-following has been proposed. • A virtual risk-based longitudinal planning model has been formulated. • Enhanced stability and improvements in safety are characteristics of the proposed planning approach. Ramp-merging zones have perennially served as bottlenecks for both safety and efficiency within road traffic. Addressing the inherent uncertainties and anomalous behaviors potentially exhibited by human-driven vehicles (HDVs) in mixed traffic flow, this study introduced a distributed merging control algorithm based on virtual risk assessment. Initially, we proposed a progressive vehicle projection method based on matching degree, formulated to engender smooth longitudinal accelerations. Building upon this foundation, a virtual risk-based longitudinal planning model named V-RQM was established, furnishing longitudinal accelerations with robust stability and safety to Connected and Autonomous Vehicles (CAVs). Results from four diverse simulation experiments corroborate that V-RQM is aptly versatile, suited for conventional one-dimensional car-following environments as well as pre-merging control in heterogeneous traffic flow. The model not only mitigates oscillatory amplitudes while sustaining high traffic efficiency but also exhibits commendable resilience against uncertainties and anomalies indigenous to mixed traffic flow. This research pioneers a novel model-driven approach to ramp-merging control, setting the stage for the full exploitation of CAV capabilities. [ABSTRACT FROM AUTHOR]

The shared autonomous vehicle (SAV), combining shared mobility and vehicle automation, will play a pivotal role in urban transportation. Effectively matching SAVs to stochastic customer demand poses a significant challenge, particularly when addressing microscopic multi-AV coordination to prevent conflicts and congestion within a network. To tackle this issue, we propose a novel traffic control scheme called the Integrated Operation Framework for Order Matching and AV Coordination (I-OFOMAC). This scheme introduces a third-party coordinator responsible for integrating online order matching for the SAV platform and real-time AV coordination for the road manager. Our approach utilizes an offline deep reinforcement learning method to train a state value neural network, which facilitates strategic order matching. Additionally, a neural network approximating bisimulation metrics is trained to enhance exploration and accelerate learning. To achieve scalability in network-level AV coordination, we adopt a scheduled transportation scheme. The coordinator solves the I-OFOMAC during each decision epoch, optimizing order matching and AV coordination. The I-OFOMAC aims to balance the goals of minimizing AV travel delay and maximizing SAV service revenue. We conduct extensive simulation studies in the realistic Wangjing network to evaluate the proposed I-OFOMAC scheme. Compared to two-step benchmark frameworks that implement order matching and AV coordination sequentially, the I-OFOMAC achieves an average increase of 13.28% in platform revenue (ranging from 2% to 22.53%) and a 4.48% reduction in average PAV travel delay (ranging from −6.55% to 20.17%). • Mixed AV zone. • Online operations of joint order matching and AV coordination. • Offline deep reinforcement learning framework. • Bisimulation-guided exploration. • A scalable approximate algorithm. • Numerical studies in realistic Wangjing network. [ABSTRACT FROM AUTHOR]

Traditional traffic signal control is one of the most widely used urban traffic control methods, due to its high efficiency, robustness, and simplicity. However, in the era of connected and automated vehicles (CAVs), it is prospective to enhance traffic control precision and achieve vehicle coordination in a more efficient manner. Focusing on an isolated intersection, this study proposes an innovative concept of Fine-grained Phase (FP) for CAV-enabled traffic control, which realizes more refined right-of-way allocation than traditional signal phases while preserving the simplicity and scalability. The development of FP jointly optimizes traffic control strategy and intersection layout design to fully utilize time-space resources, and multiple forms of FP could be generated in response to various traffic demands; meanwhile, vehicle coordination is achieved in an offline–online manner to balance solution quality and efficiency. Under the FP-based control, each lane is assigned with periodical entry time points using FP design results, then vehicles are supposed to catch up with the appropriate time points to fulfill their trips. Theoretical analyses are conducted under stationary vehicle arrival patterns, where the average delay and the intersection capacity associated with different forms of FP can be analytically acquired. For the implementation of FP-based control, we construct a general offline–online framework, consisting of an offline model that optimizes the geometry of the conflict zone at intersections and the control strategy of vehicles simultaneously, which is formulated as a mixed-integer linear programming problem, and an online FP adoption mechanism that can adapt to varying demand patterns. Under the framework, limited online computation is required, facilitating its practical real-time implementation. Simulation studies under a variety of demand settings show that FP-based control is advantageous over benchmark methods in reducing vehicle delay and also improving intersection capacity in most cases; even for cases with extremely imbalanced demands, FP-based control can admit comparable throughput, while the average delay is much smaller. In addition, results of computation time reveal that FP-based control could realize real-time implementation with negligible computation burden. • An innovative concept called FP for intersection traffic control with CAVs. • Joint optimization of traffic control and intersection layout design. • Offline–online implementation framework to balance solution quality and efficiency. • Theoretical analysis for average vehicle delay and admissible traffic capacity. • Improvement over signalized control and the state-of-art reservation strategy. [ABSTRACT FROM AUTHOR]

• An internal model-Kalman filtering-based optimal hybrid feedforward/feedback control strategy is developed. • A practically decoupled platoon regulation around an equilibrium state is established. • String stability of the control system thus obtained is analyzed. • Numerical simulations verified the theoretical analysis in decoupling between successive vehicles. This paper presents an internal model-Kalman filtering-based optimal hybrid feedforward/feedback control strategy for traffic platoon control coordination enabled by SAE Automation Level 2 or Level 3 vehicles, i.e., partially automated vehicles (PAVs). Based on the Helly linear car-following model, a PAV platoon is established. Taking each vehicle's characteristic polynomial as the dominant internal model polynomial, an augmented system state model with filtered inputs is formed, and then both the system states and the external disturbance/internal perturbation from previous vehicle can be estimated by a delicately designed Kalman filter. By using a linear quadratic regulation approach, a distributed hybrid optimal feedforward/feedback controller utilizing the local estimated states is constructed to achieve a practically decoupled platoon regulation around an equilibrium state. String stability of the control system thus obtained is further analyzed. Finally, extensive numerical simulations verified the theoretical analysis in decoupling between successive vehicles, in suppressing the influence of various external disturbances, and in maintaining string stability. [ABSTRACT FROM AUTHOR]

• Develop a framework including both stability analysis and implementation constraints for traffic platoon control via sparse autonomous vehicles. • Identify the number of human-driven vehicles that can be stabilized by one single autonomous vehicle. • Establish error dynamics in traffic platoon considering multiple disturbances and error composition. • Propose a novel ring string stability that is flexible and applicable to mixed ring traffic. The emergence of autonomous vehicles (AVs) is expected to revolutionize road transportation and it has been demonstrated that AVs can be employed as mobile actuators to effectively dissipate stop and go waves. This paper proposes a comprehensive theoretical framework including stability and implementation considerations for analyzing traffic platoon control via sparse AVs and discussing the number of HDVs that can be stabilized by one AV. Considering multiple disturbance sources, we first discuss the output error composition and analyze traffic dynamics in a platoon system. Then we discuss the Lyapunov asymptotic stability (LAS) of the system based on eigenvalue analysis and propose a novel ring string stability (RSS), which ensures the boundedness of disturbance error and guarantees that the effect of disturbance will not be amplified over a lap. The proposed RSS is flexible and applicable to mixed traffic, since the string instability for predecessor-follower pairs are allowed, provided they can be compensated by AVs. In addition, the practical constraints including driving safety and vehicle physical limitations are modeled. The worst-case evaluation of constraints is conducted to reproduce the critical scenarios in real life. Finally, we conduct numerical experiments and analyze the traffic platoon control from the aspects of both stability and practical implementations. New findings include: (1) The introduction of AVs is beneficial to dampen the waves and stabilize the HDV flow. (2) Considering the implementation issues, there is an upper bound of HDVs that can be stabilized by one single AV. (3) As the number and amplitudes of exogenous disturbances increase, it will be more challenging for sparse AVs to ensure stability and meanwhile satisfy the practical constraints. [ABSTRACT FROM AUTHOR]

The traffic capacity of signalized intersections can be increased using vehicle to infrastructure (V2I) communication by broadcasting traffic control signals to connected autonomous vehicles (CAVs). However, the potential improvement in traffic capacity with only CAVs can be substantially reduced under mixed traffic environments that include autonomous vehicles (AVs) without V2I communication. The main contribution of this work is to increase the traffic capacity (close to that achieved by CAVs with V2I communication) when there is a substantial number of AVs without V2I communication — by using a delayed-self-reinforcement (DSR) approach for vehicle spacing control with already available history of the AVs own control inputs and sensed information. Moreover, analytical estimates are established to predict the improvements in traffic capacity achieved for AVs with the DSR approach compared to the case without the DSR approach. Simulation results match the analytical predictions and show that AVs using the DSR approach (without V2I communication) are able to recover 88% of intersection capacity achieved with CAVs (with V2I communication) and result in 2.7 times the intersection capacity achieved by AVs without DSR on one way traffic. Moreover, similar improvements with DSR are shown for a real-world intersection using the Simulation of Urban MObility (SUMO) platform. AVs with DSR approach are able to recover 96% of intersection capacity achieved with CAVs (with V2I communication) and result in 1.4 times the intersection capacity achieved by AVs without DSR. Improvements using DSR are also seen with mixed traffic that includes human-driven vehicles (HDVs). [ABSTRACT FROM AUTHOR]

It has been well documented that the mixed traffic flow of human-driven vehicles (HDVs) and connected and automated vehicles (CAVs) causes an unsound communication environment and weakens the beneficial effect of CAVs. However, the communication heterogeneity in the mixed traffic, which leads to incomplete information flow topology (IFT) and further affects the processes of vehicle control and platoon operation, did not receive its deserved attention. In contrast to previous research related to the IFT and platoons in static size, this study introduced the multi-anticipation and dynamic platoons into a more complex mixture of CAVs and HDVs and jointly considered the mode transition and driver uncertainties, e.g., estimation error and driving compliance. Specifically, we proposed multi-class generic controls to represent the basic longitudinal maneuver for each type of vehicle and developed customized car-following dynamics for each controller. In order to better adapt to the communication heterogeneity in the mixed traffic, we defined a novel controller for CAVs, namely mixed AV-CAV vehicle (MACV) controller and developed the corresponding control algorithm, namely AV-CAV integrated control (ACIC) with multi-source information from both sensors system and inter-vehicle communication. Besides, another important point worth considering in mixed traffic is the spatial distribution of CAVs in platoons. As such, three platoon policies, i.e., the cross policy, the continuum policy, and the random policy, were investigated. To our best knowledge, this is a new attempt to discuss the impact of platoon policies on mixed traffic, with a combination of different IFTs. Finally, multiple traffic flow characteristics, covering robustness, efficiency, safety, and emission, were assessed by calculating their corresponding surrogate measures. Results showed that the proposal of MACV controller and ACIC was expected to efficiently prevent the deleterious degradation from the CAV controller to AV controller and construct a more stable transition for changes in vehicle controls. Furthermore, a high proportion of communicable vehicles could significantly reduce the heterogeneity of mixed traffic and improve traffic flow characteristics. Besides, this study demonstrated the outstanding performance of multi-anticipative IFTs compared to the single-anticipative IFT and revealed the advantage of cross policy in traffic robustness and safety. These findings can help provide feasible solutions for IFTs, platoon operation, and vehicle control in the mixed traffic flow and identify their respective benefits in terms of traffic flow characteristics. • Incorporate multi-anticipation and driver uncertainties into the mixed traffic. • Propose a novel controller for CAVs and develop an integrated control algorithm. • Address dynamic mode transition and platooning in the heterogeneous network. • Construct a framework for comprehensive assessment of traffic flow characteristics. [ABSTRACT FROM AUTHOR]

• Systematic review on urban traffic control under mixed traffic with CAVs. • Cover signal timing, CAV trajectory/route planning, and their cooperative control. • Summarize mixed traffic control methods at intersections with CAV-dedicated facilities. • Outline promising research directions and challenges. Efficient traffic control can alleviate traffic congestion, reduce fuel consumption, and improve traffic safety. With the development of communication and automation technologies, regular vehicles (RVs), connected vehicles (CVs), and connected and automated vehicles (CAVs) will coexist on urban roads in the near future. In this paper, we systematically review the studies on urban traffic control under the mixed traffic environment, which utilize CVs and CAVs to improve the performance of urban traffic control. We first give a glance at the evolution of urban traffic control with the deployment of CVs and CAVs to identify the important and promising research fields in the mixed traffic control. We then review the recent advances in traffic signal timing with CAV/CV trajectory data, CAV trajectory/route planning, and the cooperative control of CAV trajectories and traffic signals from the intersection level to the network level under the mixed traffic environment. Besides, we summarize two types of studies on mixed traffic control at intersections with CAV-dedicated facilities: 1) traffic signal timing and vehicle trajectory planning at intersections with CAV-dedicated lanes, and 2) deployment of CAV-dedicated facilities including CAV-dedicated lanes, links, and zones. Finally, the future research directions and challenges are discussed. We hope that this review could provide researchers with a helpful roadmap for future research on urban traffic control under the mixed traffic environment. [ABSTRACT FROM AUTHOR]

Connected automated vehicles (CAVs) have shown great potential in improving traffic throughput and stability. Although various longitudinal control strategies have been developed for CAVs to achieve string stability in mixed-autonomy traffic systems, the potential impact of these controllers on safety has not yet been fully addressed. This paper proposes safety-critical traffic control (STC) by CAVs—a strategy that allows a CAV to maintain safety relative to both the preceding vehicle and the following human-driven vehicles (HVs). Specifically, we utilize control barrier functions (CBFs) to impart collision-free behavior with formal safety guarantees to the closed-loop system. The safety of both the CAV and HVs is incorporated into the framework through a quadratic program-based controller that minimizes deviation from a nominal stabilizing traffic controller subject to CBF-based safety constraints. Considering that some state information of the following HVs may be unavailable to the CAV, we employ state observer-based CBFs for STC. Finally, we conduct extensive numerical simulations – that include vehicle trajectories from real data – to demonstrate the efficacy of the proposed approach in achieving provably safe traffic. [ABSTRACT FROM AUTHOR]

• A lane-level VSL (LVSL) approach with actor-critic framework is proposed to provide differential VSL control. • A hybrid reward function is presented to adjust relationship between the reducing crash risk and improving traffic efficiency. • A twin delayed deep deterministic policy gradient (TD3)-based LVSL approach is established for the first time. • The TD3LVSL shows better performance than baseline models in terms of improving traffic safety and traffic efficiency. Variable speed limit (VSL) control plays a vital role in the emerging connected automated vehicle highway (CAVH) system, which can alleviate recurrent traffic congestion caused by capacity drop and accidents. However, the effect of the VSL on improving traffic efficiency is still controversial. It is necessary to study how to explore the potential benefits of the VSL by balancing its influence on reducing crash risk and enhancing traffic efficiency. To fill the technological gap above, this paper proposes a reinforcement learning-based lane-level VSL (LVSL) control approach to conduct refined traffic control on the mainlines. Firstly, an actor-critic framework is developed to generate and evaluate the discrete speed limits of each lane in continuous action space. To optimize traffic control performance, a hybrid reward function is developed by synchronously considering traffic safety and traffic efficiency of the bottleneck area. Then, to solve the overestimation bias problem of the actor-critic methods caused by function approximation error, a twin delayed deep deterministic policy gradient (TD3) method is introduced to train the framework of the LVSL method. Finally, a real-world recurrent bottleneck of the State Route 91 highway in California is simulated with consideration of the connected automated vehicles to examine the performance of the TD3-based LVSL (TD3LVSL) controller. The simulation results reveal that the proposed method is capable of reducing crash risk and improving traffic efficiency synchronously. Compared with the LVSL controller based on the deep deterministic policy gradient approach, the TD3LVSL controller shows better performance in terms of traffic safety and efficiency. These findings indicate that the proposed controller could contribute to future traffic control in a CAVH environment. [ABSTRACT FROM AUTHOR]

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]

• A two-layer hierarchical model is developed to coordinate adaptive CAV platoons. • DRL is applied to implement centralized and decentralized control strategies. • The optimal size of CAV platoon is determined by considering multiple objectives. • COOR-PLT has satisfactory convergence performances and the capability of avoiding deadlocks. • COOR-PLT outperforms state-of-the-art methods on reducing travel time and fuel consumption in various traffic conditions. Platooning and coordination are two implementation strategies that are frequently proposed for traffic control of connected and autonomous vehicles (CAVs) at signal-free intersections instead of using conventional traffic signals. However, few studies have attempted to integrate both strategies to better facilitate the CAV control at signal-free intersections. To this end, this study proposes a hierarchical control model, named COOR-PLT, to coordinate adaptive CAV platoons at a signal-free intersection based on deep reinforcement learning (DRL). COOR-PLT has a two-layer framework. The first layer uses a centralized control strategy to form adaptive platoons. The optimal size of each platoon is determined by considering multiple objectives (i.e., efficiency, fairness and energy saving). The second layer employs a decentralized control strategy to coordinate multiple platoons passing through the intersection. Each platoon is labeled with coordinated status or independent status, upon which its passing priority is determined. As an efficient DRL algorithm, Deep Q-network (DQN) is adopted to determine platoon sizes and passing priorities respectively in the two layers. The model is validated and examined on the simulator Simulation of Urban Mobility (SUMO). The simulation results demonstrate that the model is able to: (1) achieve satisfactory convergence performances; (2) adaptively determine platoon size in response to varying traffic conditions; and (3) completely avoid deadlocks at the intersection. By comparison with other control methods, the model manifests its superiority of adopting adaptive platooning and DRL-based coordination strategies. Also, the model outperforms several state-of-the-art methods on reducing travel time and fuel consumption in different traffic conditions. [ABSTRACT FROM AUTHOR]

• A data-driven route flow estimation framework is proposed using data from automatic vehicle identification sensors and probe vehicle trajectories. • A prior-information-free objective function is constructed using the notion of flow probe vehicle route penetration rate and route travel time. • The information of observable route flow and observable route travel time is integrated into the estimation model. • The solution uniqueness is guaranteed though an iterative procedure under the entropy maximum principle. Accurate estimation of route flow is crucial to traffic control optimization. Existing route flow estimation models are typically built upon strong assumptions on traveler route choice behavior or turn to data-driven approaches which heavily rely on prior OD estimation and high sensor coverage rate. This study proposes a route flow estimation framework by fusing the probe vehicle trajectory and automated vehicle identification (AVI) data. The notion of probe vehicle route penetration rate (RPR) is utilized for the construction of a novel data-driven objective function. Sensor-OD flow and observable route flow penetration are formulated as model constraints. The solution uniqueness is guaranteed though an iterative procedure under the principle of entropy maximization. Numerical studies are conducted based on pNEUMA, a large-scale full sample trajectory dataset. Results show that the proposed model performs better than the traditional path flow estimation model, especially when the prior flow information is not available. Sensitivity analysis on AVI sensor coverage rate, probe vehicle route coverage rate and RPR distribution demonstrates the proposed model's applicability in practice. [ABSTRACT FROM AUTHOR]

Connected and automated vehicle (CAV) technology is widely recognized as a promising way to improve traffic safety, fuel efficiency and network throughput. However, at the early stage of CAV adoption, there will inevitably be a long period when both CAVs and regular vehicles coexist in the traffic system. To facilitate CAV functionalities in mixed traffic, providing dedicated rights of way for CAVs is necessary. In such scenarios, traffic control requires careful design in the weaving areas that allow CAVs to transfer between dedicated roads and ordinary roads for both safety and efficiency considerations. This paper proposes a dynamic programming approach for the coordinated trajectory planning of CAVs in mandatory lane-changing scenarios, in which CAVs have to switch from a dedicated CAV lane into parallel ordinary lanes with mixed traffic. For a group of CAVs that are poised to enter the ordinary road, the proposed method aims to provide each CAV with a suggested lane-changing gap and a reference trajectory in a coordinated manner. To solve the problem, we establish a dynamic programming formulation to determine the suggested gaps for all CAVs. We formulate a nonconvex quadratic-constrained quadratic programming (QCQP) problem to characterize the trajectory determination problem, and a relaxation-based approximate algorithm is developed to solve the QCQP. Simulation results show that, compared to the benchmark methods, the proposed method offers substantial benefits both in terms of traffic efficiency and in-vehicle passengers experiences. • Planning of lane-changing from dedicated CAV lanes to non-cooperative HDV traffic. • Coordinated optimization toward efficient and smooth lane-changing trajectories. • Novel algorithm to decompose optimization problems based on dynamic programming. [ABSTRACT FROM AUTHOR]

• Propose a cooperative signal-free intersection control framework under V2X communications. • Proposed framework integrates virtual platooning control and traffic flow regulation. • Virtual platooning controller mitigates effects of parametric inaccuracies and unparameterized disturbances. • Virtual platooning controller guarantees string stability. • Traffic flow regulation alleviates traffic spillbacks and travel delays. The emerging technologies of connectivity and automation enable the potential for signal-free intersection control. In this context, virtual platooning is posited to be an innovative, decentralized control strategy that maps two-dimensional vehicle movements onto a one-dimension virtual platoon to enable intersection operations. However, the effectiveness of virtual platooning-based control can be limited or degraded by parametric inaccuracies and unparameterized disturbances in vehicle dynamics, heavy traffic congestion, and/or uncoordinated platoons in multi-lane intersections. To explicitly address these limitations, this study proposes a hybrid cooperative intersection control framework consisting of microscopic-level virtual platooning control and macroscopic-level traffic flow regulation for traffic environments with connected autonomous vehicles. In virtual platooning control, vehicles approaching an intersection are organized into coordinated independent virtual platoons to avoid potential conflicts triggered by platoon formation changes. Through coordination, vehicles in a platoon are grouped into compatible passing sets to maintain desired safe spacing when proceeding through the intersection. We propose a distributed adaptive sliding mode controller (DASMC) which uses the backstepping control method and model reference adaptive control method to address parametric inaccuracies, and the sliding mode control method to consistently suppress the negative effects of the unparameterized disturbances. Each vehicle approaching the intersection utilizes the kinematic information from neighboring vehicles to implement the DASMC in a distributed manner such that vehicles within the same virtual platoon can achieve consensus safely. However, virtual platooning control cannot preclude excessive traffic from approaching the intersection, which can cause undesired spillbacks and degrade intersection control performance. To address this issue, traffic flow regulation is integrated with the virtual platooning control using an iterative feedback loop mechanism. In each iteration of the iterative feedback loop, a constrained finite-time optimal control (CFTOC) problem is solved to determine the optimal input flow permitted to proceed through the intersection, and the virtual platooning control provides feedback on the queue status to the CFTOC to initiate the next iteration. The effectiveness of the proposed intersection control framework is evaluated through numerical experiments. The results indicate that the proposed virtual platooning DASMC controller can mitigate the effects of parametric inaccuracies and unparameterized disturbances to achieve consensus for approaching vehicles, as well as guarantee string stability. Further, the proposed framework can alleviate traffic spillbacks and travel delays effectively through traffic flow regulation. [ABSTRACT FROM AUTHOR]

• Develop a three-level model to integrate signal timing and CAV trajectory planning. • Left-turn and through CAVs share CAV-dedicated lanes with trajectory planning. • CAVs cross the intersection during shared phases with HVs. • Propose a repeated-barrier-group cycle structure in signal timing. • Design a rolling-horizon scheme to dynamically apply the model with varying traffic. Connected and automated vehicles (CAVs) and human-driven vehicles (HVs) are expected to coexist in the near future. CAV-dedicated lanes and phases have been explored to handle the uncertainty in the driving behavior of HVs in the mixed traffic environment. However, CAV-dedicated phases could significantly sacrifice HV benefits. This study proposes a shared-phase-dedicated-lane (SPDL)-based traffic control model at an isolated intersection with CAV dedicated lanes in the mixed traffic environment. In the proposed model, left-turn and through CAVs share CAV-dedicated lanes and cross the intersection during the shared phases with HVs. A three-level optimization model is developed. At the upper level, a standard NEMA (National Electrical Manufacturers Association) ring barrier structure is used for the signal optimization and barrier durations are optimized by dynamic programming to minimize the total vehicle delay. At the middle level, phase sequences and phase durations are optimized by enumeration for the given barrier from the upper level and the minimum vehicle delay is fed to the upper level. At the lower level, CAV platooning in the buffer zone and trajectory planning in the passing zone are conducted based on the signal timings of the barrier from the middle level and the travel time of CAVs is fed to the middle level. A rolling-horizon scheme is designed for the dynamical implementation of the proposed model with time-varying traffic conditions. The proposed model is applicable to intersections with more than two lanes in each approach and without shared lanes by left-turn and through HVs, which are commonly seen critical intersections in urban traffic control. Numerical studies validate the advantages of the SPDL-based control over the blue-phase-based control in previous studies in terms of average vehicle delay and intersection capacity. Further, the SPDL-based model is extended to serve as an alternative approach without the buffer zone. [ABSTRACT FROM AUTHOR]

• This paper investigates the key ways for automated vehicles (AVs) to improve the traffic efficiency of two-dimensional urban network traffic. • Different cooperative driving strategies are investigated in a systematic and comprehensive way, and we find that the passing order has a dominant impact on the network traffic efficiency. • This paper compares the differences and characteristics of the impact of the passing orders and the car-following gaps on the urban network traffic. • By using the macroscopic fundamental diagram (MFD) as the performance metric, we find that a better passing order can broaden the free flow region and improve the maximum flow, which is of great benefit to the network traffic efficiency. Cooperative driving, especially the passing order, is the critical link to improve the traffic efficiency of the road network by using automated vehicles (AVs). However, most studies have only considered the performance of the passing order at the isolated intersection and have not yet investigated its impact on the road network. In this paper, we will focus on the performance of the passing orders derived from different cooperative driving strategies on the network traffic through a series of simulation experiments. Meanwhile, we will compare the impacts of the passing order at intersections and the car-following gap in straight links on the network traffic efficiency. The experiments results show that the passing order has a dominant impact on the network traffic efficiency, and a better order can significantly raise the curve of the macroscopic fundamental diagram (MFD); due to the inevitable conflicts in the two-dimensional traffic, the choice of the car-following gap within a reasonable range has a relatively small improvement on the network traffic efficiency. The findings in this paper have instructive significance for the rising research on network-wide cooperative driving and provide a systematical perspective for network traffic control. [ABSTRACT FROM AUTHOR]

Connected and automated vehicles (CAVs) promise to significantly improve road traffic. To a certain extent, this situation is similar to the expectations at the end of the last century about the positive effects that the introduction of Adaptive Cruise Control (ACC) systems would have had on motorway traffic. The parallelism is interesting because ACC equipped vehicles represent the first level of vehicle automation and are now widely available on the market. In this light, studying ACC impacts can help to anticipate potential problems related to its widespread application and to avoid that AVs and CAVs will lead to the same problems. Only a few test-campaigns had been carried out studying the ACC impacts under real-world driving conditions in quantitative terms. To bridge this gap, the Joint Research Centre of the European Commission has organized a number of experimental campaigns involving several ACC-equipped vehicles to study different implications of their widespread. In this context, the present paper summarizes the outcomes of a test campaign involving 10 commercially available ACC-equipped vehicles. The test campaign has been executed in two different test-tracks of the ZalaZONE proving ground, in Hungary. The tests have been carried out at low-speeds (30–60 km/h) and have involved platoons of vehicles of different brands and different powertrains, which were tested in a variety of vehicle orders and with different settings of their ACC systems. Test results have been used to derive information about the properties of the different ACC systems, to study their string stability, to study the effect of ACC systems on traffic flow, and to draw inference about the possible implications on energy consumption and traffic safety. Results confirm the previous findings in terms of string instability of the ACC and highlight that in the present form, ACC systems will possibly lead to higher energy consumption and introduce new safety risks when their penetration in the fleet increases. However, they also highlight that the materialization of the above findings for AVs depends on the operational logic that manufacturers will adopt during the implementation phase. Therefore, results suggest that functional requirements to guarantee string stability and in general to not disrupt the normal flow of traffic should be introduced both for ACC and for any automated system that will be placed on the market in the future. [ABSTRACT FROM AUTHOR]