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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]

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]