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To explore and evaluate the impacts of relative velocity difference (RVD) with memory on the dynamic characteristics and fuel economy of traffic flow in the intelligent transportation environment, we first analyze the linkage between RVD with different-step memory and the following car’s behaviors with the measured car-following (CF) data in cities by using the gray correlation analysis method and then present a RVD model based on the previous CF models in the literatures and calibrate it. Finally, we conduct several numerical simulations in the adaptive cruise control (ACC) strategy to explore how RVD with memory affects car’s velocity fluctuation and fuel consumptions, and find that the RVD model can describe the phase transition of traffic flow and estimate the evolution of traffic congestion, and that considering RVD with memory in modeling CF behaviors and designing the advanced ACC strategy can improve the stability and fuel economy of traffic flow.

This study proposes a Multi-agent Fusion Double-Dueling-Deep Q-Network Traffic Flow (MF3DQN-TF) for Connected and Autonomous Vehicles (CAVs) in mountain tunnel entrance sections, considering nonlinear coupling effects. Complex road conditions and nonlinear coupling effects of tunnel exit flow often cause unstable traffic flow there, impacting traffic efficiency and safety. The new framework, combining multi - agent deep reinforcement learning and attention mechanisms, has shown marked improvements over traditional rule - based regulation methods in various traffic scenarios through comparative experiments. Actual simulations indicate it can boost traffic flow stability by over 15%, vehicle efficiency by about 20%, and cut congestion time by 18%. Specifically, it enhances average vehicle speed by 25% and reduces the traffic congestion index by 22% compared to conventional methods. The attention mechanism improves intelligent agents’ decision - making efficiency, enabling real - time vehicle interaction and coordination optimization, thus enhancing intelligent transportation systems’ overall adaptability. The framework helps stabilize traffic flow and ease common traffic issues at mountain tunnel entrances, strongly supporting intelligent transportation system development.

This paper investigates the string stability and traffic flow stability based on an improved quadratic spacing policy for heterogeneous vehicular platoons with actuator faults. Due to the occurrence of actuator faults, the maximum acceleration changes, which may invalid the traditional quadratic spacing policy. To tackle the dilemma, an improved quadratic spacing policy with the lower bound of fault factor is proposed. Furthermore, the improved quadratic spacing policy removes the assumption of zero initial spacing errors. Then, by employing the adaptive fuzzy logic system technique and the PID-type sliding mode control method, an adaptive fuzzy fault-tolerant controller is designed to guarantee individual vehicle stability and string stability after reaching the sliding surface. In addition, traffic flow stability is also ensured thanks to the improved quadratic spacing policy. Finally, simulation results have demonstrated the reliability and effectiveness of the presented method.

Connected and autonomous vehicle (CAV) technologies are likely to be gradually implemented over time. In this paper, an adaptive cruise control, named Smart Driver Model (SDM), is proposed to describe the autonomous vehicles flow. The stability criteria is proposed for SDM to judge the stability of homogeneous traffic flow. Numerical simulations were conducted to verify the results of the theoretical analysis. Single-lane vehicle dynamics in a traffic stream with connected and autonomous vehicles are simulated by varying model parameters. Simulation results are consistent with the results of linear stability analysis. As a result, a set of parameters is proposed to investigate the stabilization effect of the proposed model on homogeneous traffic flow considering realistic driving cycle and cut-in condition. By simulating a platoon with a lead vehicle which follows the Urban Dynamometer Driving Schedule (UDDS), we find out that the proposed model can stabilize the traffic flow with proposed parameters. The results from simulation and linear stability analysis show that SDM outperforms the IDM-ACC and the ACC proposed by Milanés and Shladover in terms of stabilization effect on homogeneous traffic flow. The simulation result shows that the SDM-equipped vehicles are able to stabilize the homogeneous traffic flow under cut-in condition.

Extended transit time and increased consumer expectations arouse an interest in passenger comfort research. Few studies have been conducted on passenger comfort of Connected and Automated Vehicles (CAV) traffic flow, thereby leaving a research gap. This paper focuses on filling this research gap and evaluating CAV impact on passenger comfort from the traffic flow perspective. Specifically, optimal stability of traffic flow mixed with Manual Driven Vehicles (MDV) and CAV is desired to improve passenger comfort. For describing stability condition of the mixed traffic flow, in which multiple connected feedbacks of CAV exist with Vehicle-to-Vehicle (V2V) communications, local vehicular platoons with uniform structure are considered to be the optimization objective. Its stability charts with respect to equilibrium speeds and CAV feedback gains are calculated based on transfer function theory, thereby controlling CAV feedback gains for optimal stability. The CAV impact on the passenger comfort is evaluated under optimal control results of CAV feedback gains, by using numerical simulations under car-following models. It is indicated that stability optimization benefits passenger comfort of the mixed CAV traffic flow.

Connected and automated vehicles (CAVs) represent a significant development in the transport industry owing to their intelligent and interconnected features. Potential field theory has been extensively used to model CAV driving behaviour owing to its objectivity, universality, and measurability. However, existing car-following models do not consider the impact of time delays and the influence of information from multiple vehicles ahead and behind. This paper focuses on the driving-safety risks associated with CAVs, aiming to enhance vehicle safety and reliability during travelling. We developed a multi-vehicle car-following model based on safety potential fields (MIDM-SPF), taking into account the characteristics of multi-vehicle connected information and time delays. To enhance the model’s precision, real-world data from urban roads were employed, alongside an improved optimisation algorithm to fine-tune the car-following model. The simulation experiment revealed that MIDM-SPF significantly reduces stop-and-go traffic, thereby improving traffic flow stability in urban areas. Additionally, we validated the stability of our model under varying market penetration rates in large-scale mixed traffic. Our findings indicate that increasing the CAV proportion improves the stability of mixed traffic flows, which has important implications for alleviating traffic congestion and guiding the large-scale implementation of autonomous driving in the future.

Stability analysis of a single-lane microscopic car-following model is studied analytically from the perspective of delayed reactions of human drivers. In the literature, the delayed reactions of the drivers are modeled with discrete delays, which assume that drivers make their control decisions based on the stimuli they receive from a point of time in the history. We improve this model by introducing a distribution of delays, which assumes that the control actions are based on information distributed over an interval of time in history. Such an assumption is more realistic, as it takes into consideration the memory capabilities of the drivers and the inevitable heterogeneity of their delay times. We calculate exact stability regions in the parameter space of some realistic delay distributions. Case studies are provided demonstrating the application of the results.

Adaptive cruise control (ACC) is one of the modern vehicle digitalization inventions over the world. It automatically adjusts the speed of a subject vehicle to match the speed of the vehicle in front of the subject vehicle. The ACC systems will be the more advanced driving assistant techniques in the next generation of Bangladesh. Therefore, a broad review on the ACC systems has been performed in the present study. In this study, the past researches on ACC systems over the world, the present scenario of ACC systems in Bangladesh, and future challenges in Bangladesh to implement ACC systems have been studied in detail. From the study, it is noticed that ACC systems were used in the world in the very beginning of 1995. These systems have been paid high attention to reduce the accident and accidental death rate, to save the energy, to reduce the impacts on the environment, and to fulfill the sustainable development goals (SDGs) declared by the United Nations (UN). It was first imported to Bangladesh in 2015, but most of the drivers do not feel comfortable on applying it. Studies have found some drawbacks of the ACC systems in banking of roads, turning points on roads, and making drivers less conscious. The system also has some issues in terms of traffic flow stability and control over lane positions. This article also attempts to summarize the current road condition, workability of ACC systems, drawbacks of ACC, the traffic flow stability, and existing challenges for ACC systems in Bangladesh’s road conditions.

The drivers in actual traffic differ in the aggressiveness in driving behaviors, which will finally be reflected in the interaction between vehicles and fluctuations in flows. In this paper, we try to study the effect of driving aggressiveness on the traffic stability by proposing an extended microscopic car-following model, in which the optimal velocity is reconstructed to divide the drivers in traffic system into two groups according to driving aggressiveness of each individual. The stability condition of the proposed model is derived to explore its ability against a small perturbation by use of the linear stability theory. We obtain the neutral stability lines for different percentages of drivers with a higher driving aggressiveness, finding that the traffic flow trends to stable with the increase in the percentage for higher driving aggressiveness drivers when the average headway is less than a critical value or greater than another critical value, but when the average headway falls into the intermediate range between the two critical values, the traffic flow becomes more and more unstable with increase in the percentage of drivers with a higher driving aggressiveness. Finally, numerical simulations are conducted to verify these theoretical results and examine how the percentage of vehicles driven by higher driving aggressiveness drivers affects the traffic flux of the vehicle system.

Uphill and downhill roads are prevalent in mountainous areas and freeways. Despite the advancements of vehicle-to-vehicle (V2V) communication technology, the driving field of vision could be still largely limited under such a complex road environment, which hinders the sensors from accurately perceiving the speed of front vehicles. As such, a fundamental question for autonomous traffic management is how to control traffic flow associated with the velocity uncertainty of preceding vehicles? This paper aims to answer this question by devising a cooperative control method for autonomous traffic to stabilize the traffic flow under such a complex road environment. To this end, this paper first develops a traffic flow model accounting for the uncertainty of preceding vehicle’s velocity on gradient roads and further devises a new self-delayed feedback controller based on the velocity and headway differences between the current time step and historical time step. The sufficient condition where traffic jams do not occur is derived from the perspective of the frequency domain via Hurwitz criteria and H ∞ norm of transfer functions. The Bode diagram reveals that the robustness of the closed-loop traffic flow model can be significantly enhanced. Simulation results show that the key parameters (control gain coefficient and delay time) of the designed controller contribute to the stability of traffic flow, which is consistent with the theoretical analysis conclusion.