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In the environment of intelligent connected vehicles, the mixed traffic flow consisting of human-driven vehicles (HDVs), connected and automated vehicles (CAVs), and their platoons will coexist for a long time. Exploring and discovering the operational rules of mixed traffic flow is the foundation for its management and control. This study delves into the distinct characteristics of various vehicles within a mixed traffic environment, particularly focusing on HDVs, CAVs, and CAV platoons. Drawing on the psychological predisposition of HDVs to maintain a prudent following distance, we introduce a discrete motion safety distance model to establish the longitudinal movement rule for HDVs. Furthermore, a longitudinal movement rule for CAVs is established based on their ability to keep a constant headway using Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), alongside meeting specific speed and spacing criteria for platoon movement. On this basis, the paper proposes a cellular automaton model for single-lane mixed traffic flow that accounts for CAV platoon length and the decision-time disparity between HDVs and CAVs. The simulation results show that an increased proportion of CAVs can enhance the traffic capacity, elevate average speed, extend average platoon length, and reduce the congestion ratio and CACC degradation rate. Notably, in scenarios where the CAV proportion p > 0.7, the flow-density graph shows a trapezoidal shape, with the majority of CAVs in the traffic flow synchronizing their movements in CACC mode. Even when vehicular density surpasses the optimum threshold, the traffic flow maintains high-speed operation until reaching a critical congestion point, whereupon velocities precipitously decline. Through sensitivity analysis aimed at understanding the impact of maximum platoon length, it has been observed that, for CAV proportions below 0.7, variations in maximum platoon length negligibly impact traffic capacity, velocity, CACC degradation, and congestion levels. Only in scenarios dominated by CAVs does extending platoon length restrictions significantly enhance performance. However, as the maximum platoon length increases, the gains in traffic capacity, speed, CACC degradation rate, and congestion ratio diminish. When the platoon length of CACC exceeds a certain threshold, there will not be a significant improvement in traffic capacity or speed. [ABSTRACT FROM AUTHOR]

In this study, based on the comprehensive analysis of asymptotic stability and damping characteristics for the Intelligent Driver Model (IDM) car-following strategy, we propose a self-adaptive IDM (SA-IDM) car-following strategy, which is specifically designed for adaptive cruise control (ACC) vehicles. Using a coefficient of self-adaption, SA-IDM strategy can adaptively adjust the acceleration control function of following vehicle in real time given the velocity of preceding vehicle and time headway, in order to guarantee the asymptotically stable and overdamped condition for the vehicle platoon under any circumstances. The results of simulation experiment for a vehicle platoon with NGSIM dataset indicate that, vehicles can drive more stably and smoothly under traffic perturbation using the proposed SA-IDM strategy than the original IDM strategy, as well as the existing IDMM and E-IDM strategies. Meanwhile, SA-IDM strategy helps to improve the driving safety of vehicle platoon considerably. Overall, SA-IDM strategy provides a promising solution with higher stability, reliability, and safety for the longitudinal car-following control in the roadway traffic. • A self-adaptive IDM car-following strategy considering asymptotic stability and damping characteristics is proposed. • A coefficient of self-adaption in SA-IDM strategy guarantees vehicle's asymptotically stable and overdamped condition. • Vehicles drive more stably and smoothly using SA-IDM strategy under traffic perturbation. • SA-IDM strategy improves the driving safety of vehicle platoon considerably under traffic perturbation. [ABSTRACT FROM AUTHOR]

Considering the influence of slope sections and random degradation of the communication function of cooperative adaptive cruise control vehicles and the effect of the cooperative adaptive cruise control vehicle group driving, a linear stability criterion for heterogeneous traffic flow composed of cooperative adaptive cruise control vehicles and humanly-driven vehicles on slopes is derived, and a parameter sensitivity analysis is carried out. On this foundation, the safety of traffic flow on two-lane roads up and down hills is simulated while considering the lane-changing behavior of automobiles. The stability study shows increased cooperative adaptive cruise control vehicle penetration and uphill gradient will help improve traffic flow stability. In contrast, the increase in the downhill slope will intensify the transmission amplitude of the disturbance. When vehicle starting sensitivity is 2.5, and the gradient is 60, the minimum cooperative adaptive cruise control vehicle penetration required for mixed traffic flow to maintain stability on a downhill at any initial equilibrium speed is 85%, and the minimum penetration required on an uphill is 73%, and this proportion decreases with the increase in the uphill gradient and increases with the rise in the downhill slope. According to the safety study, increasing cooperative adaptive cruise control vehicle penetration and implementing lane-changing rules will improve traffic flow safety. Compared with the straight section, under the same conditions, the traffic flow safety of the uphill section is the best, and the safety of the downhill section is poor, which is consistent with the stability analysis results. This study is of great significance for future traffic control strategies for mountain roads. [ABSTRACT FROM AUTHOR]

• CAV data enabled adaptive signal control strategies to enhance safety environment. • Innovated mechanism and data models to evaluate CAV data potentials for safety. • SSAM integrated into the developed data fusion models to support safety evaluation. • The proof-of-concept study suggests positive benefits from operation and safety. Given the connected and autonomous vehicle (CAV) generated trajectories as a "floating sensor" data source to obtain high resolution CAV-generated mobility data at intersections, to ensure maximum safety effect while maintaining efficient operations at the same time is actually a complex task in traffic management. Literature indicates that methods for evaluating the CAV-generated data potentials focusing on safety benefits are still immature. The primary reason lies in lack of underlying mechanism and data models to make the data intelligent to enhance safety environment through adaptive traffic signal control. On top of the developed intelligent CAV-generated mobility data fusion model framework in support of adaptive traffic signal control, parameters and models included in Surrogate Safety Assessment Model (SSAM) are integrated to indicate the risk of near crashes and then evaluate the safety environment. A proof-of-concept study is conducted in Uptown Cincinnati, Ohio to test the developed data fusion models in terms of safety enhancement, along with operational benefits. In the tests, the CAV-generated data supported developed adaptive signal plan is compared with the basic signal plans (i.e., pretimed signal plan, actuated signal plan) that supported by traditional detection systems. The results indicate that the adaptive signal plan has a great potential to decrease at most 91% of total collision risk (measured in probability), 71% of crossing collision risk, 90% of rear end collisions risk and 100% of lane-changing collisions risk, compared with basic signal plans. Meanwhile, it increases up to 6.8% of throughput, and decreases up to 91.49% of average delay, 96.23% of queue length and 75.00% of number of stops. The benefits of operation efficiency include reduced average delay and reduced number of stops; but no improvement in reducing collisions severity that is reflected by high maximum speed and relative speed of two vehicles involved in a potential collision. [ABSTRACT FROM AUTHOR]

The optimization of intersection signal control can improve traffic efficiency, reduce congestion degree, and improve traffic safety. Aiming at implementing the coordinated adaptive traffic signal control (ATSC) across large-scale arterial network, multi-agent reinforcement learning (MARL) has been widely concerned and lucubrated. Nevertheless, the existing MARL-based ATSC studies suffers from several limitations: (1) While most existing researches focused on the mobility performance of controlled corridor, there calls for a methodology that aims at combine multi-objective performance on traffic safety, efficiency, and network coordination simultaneously; (2) Most methods ignore the correlations between multiple agents, nor considers the spatial–temporal dependencies among the corelated neighboring intersections due to high communications requirements, which can hardly be achieved in real adaptive coordination control. To overcome the aforementioned difficulties, a multi-objective reinforcement learning model (NACRL) for network-wide coordinated signal control is proposed. Firstly, to enforce a coordinated network control with safety and efficiency considerations, a reward mechanism inspecting both traffic safety and traffic efficiency indicators was designed to achieve ideal performance in terms of mobility, safety and smooth. Secondly, the proposed NACRL conducted a centralized training-decentralized execution framework, this overcomes the critical limitation of data transmission in the field implementation while explicitly analyzing the traffic state over the entire network instead of examining each isolated intersection. Last but not least, the proposed algorithm utilized the attention mechanism to dynamically capture the sophisticated spatial–temporal dependencies over the complex arterial network, which aids the better coordinated control over multi-agents deployed at the intersections across the corridor. To testify the effeteness of the proposed algorithm, extensive experiments were implemented in both large-scale synthetic traffic grid and real-world arterial network. The experiment demonstrated that the proposed NACRL algorithm outperforms other state-of-the-art baselines with simultaneously improved performance in terms of traffic safety, traffic efficiency and network coordination, as well as improved algorithm convergence and interpretability. [ABSTRACT FROM AUTHOR]

A formation tracking control problem for uncertainty traffic cone robots (TCRs) is considered. In this paper, the TCRs can not only perform formation behaviors but also track placement targets under environmental constraints (i.e., the position constraints in the y -direction.). To deal with the environmental constraints, a differential homeomorphism transformation is selected to eliminate the constraints. Moreover, the uncertainty of the TCRs is bounded, including matching portions and mismatching portions. For the matching uncertainty, a series of adaptive parameters are introduced to estimate the real-time information of the uncertainty. The mismatching uncertainty is tackled by a geometric decomposition, which is orthogonal to the range space of the formation. At the same time, a fuzzy uncertainty optimization design problem is presented. The global solution to the resulting optimization problem always exists and is unique. Using adaptive robust control, TCRs' performance is deterministically and fuzzy-optimized. The system performance is demonstrated by a range of numerical simulations and real experiments with three TCRs. • An adaptive robust formation tracking control design method is proposed. • The artificial potential field function has the property of "repulsion at near and attraction at far". • The uncertainty of the TCR system is divided into a matching part and a mismatching part. • A constrained optimization problem based on uncertain fuzzy information is presented. [ABSTRACT FROM AUTHOR]

Cooperative adaptive cruise control (CACC) has been regarded as a promising approach to improving traffic stability and safety with vehicle-to-vehicle communication. However, current communication system is vulnerable to all kinds of cyberattacks, bringing potential security risks to the transportation system. Although cyberattacks have been well documented recently, realistic impact of cyberattacks is not replicated by reason of: First, real-world cyberattacks can take various attack forms and cause diverse damages, but there has been limited research in generalizing their effects on inter-vehicle communication or providing a comprehensive investigation into the resulting response in vehicle dynamics. Second, some adaptive traffic operations, e.g., platooning under a connected environment and dynamic controls under disturbance, were usually neglected. Third, cyberattacks were thought to have constant properties in terms of attack targets and attack severity, which goes against the stochastic nature of cyberattacks. To this end, the objective of this study is to address these existing gaps by generalizing multi-class stochastic cyberattacks and examining their impact in a more realistic traffic stream. Specifically, we categorized different cyberattacks as bogus messages, delay/replay, and malicious messages, and proposed their general forms with stochastic noise or failures on control variables, i.e., location, velocity, and acceleration. Moreover, the research scenario was expanded to a heterogeneous traffic comprising of CACC vehicles, ACC vehicles, and manually driven vehicles, incorporating adaptive traffic operations such as dynamic controls, vehicle degradation, and platooning. Finally, we analysed​ the rear-end collision risks in the presence of multi-class cyberattacks with surrogated safety measures and identified the most critical circumstances under the cyberattack combinations. The findings provide evidences for the adverse effects of cyberattacks on velocity oscillation and collision risks, based on our general modelling of cyberattacks in a more practical scenario. • Propose a framework for the general modelling of multi-class stochastic cyberattacks. • Extend the stochasticity of cyberattacks to stochastic targets and stochastic severity. • Expand the cyberattack scenarios to heterogeneous traffic with dynamical controls. • Assess the impact of cyberattacks on both velocity oscillation and collision risks. [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]

• Reinforcement learning (RL)-based longitudinal control for absorbing shockwaves. • Cooperative control benefits both ego and surrounding vehicles. • RL controllers trained on HighD trajectories generalize well to commercial ACC data. • Taking information from two lead vehicles further improve RL model performance. Stop-and-go traffic poses significant challenges to the efficiency and safety of traffic operations. In this study, a cooperative longitudinal control based on Soft Actor Critic (SAC) Reinforcement Learning (RL) is proposed to address this issue. The reward function is carefully designed to consider vehicle cooperation and to achieve three main objectives: safety, efficiency, and oscillation dampening. A global performance metric for oscillation dampening is proposed to evaluate the developed RL and other baseline models. Depending on the number of preceding vehicles that can share maneuver information, two models RL-1 and RL-2 are proposed and compared with human driven (HD) and an adaptive cruise control (ACC) model using the HighD and simulated data. It is found that with information from additional preceding vehicles, RL-2 can dampen shockwaves more efficiently. Specifically, RL-1 and RL-2 decrease traffic oscillation by 15%-36% and 15%-42%, respectively, while HD amplifies the oscillation by 14–37%. The ACC model can also dampen shockwaves but is not as effective as RL-1 and RL-2. The two RL control methods are further evaluated based on data collected using a commercial Model X vehicle. Compared with the commercial Model X ACC vehicle in some controlled settings, the proposed RL methods can better dampen the stop-and-go waves by generating smaller oscillation growth, overshooting, and average acceleration/deceleration rate change, suggesting that they can generalize well in a new but similar environment. Finally, the RL methods are evaluated considering a platoon of vehicles with different RL penetration rates. The results show that they consistently outperform HD and ACC in dampening shockwaves. [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]

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]

This paper presents an adaptive signal controller for managing traffic delays and urban bus service reliability with fully adaptable acyclic timing plans. The signal controller is built upon a reinforcement learning framework that consists of a model-based and a data-driven component. The model-based component is represented by a hybrid kinematic wave traffic model that integrates macroscopic flow-based and microscopic vehicle-based state variables subject to stochastic demands and bus service status. To cope with the high dimensional solution space, the data-driven component is incorporated as a multi-layer artificial neural network and is used to approximate future traffic states and system performances with respect to prevailing control settings. Before the controller can be used, the neural network is to be trained through a series of realised dynamic state transitions via an on-policy temporal difference learning algorithm. The proposed control framework is tested over a real world corridor scenario in London, UK. The proposed controller is able to reduce both traffic delays and bus service variabilities subject to stochastic demands with acyclic timing plans that can be derived in short computational time. This study contributes to the design of adaptive network traffic control for multi-modal networks with connected vehicle technology and advanced learning-based optimisation techniques. [ABSTRACT FROM AUTHOR]