Traffic flow stability is fundamentally a nonlinear dynamic phenomenon, where the amplification of small disturbances into stop-and-go waves directly determines road efficiency, safety, and sustainability. This paper provides a systematic review of traffic flow stability research through the lens of nonlinear dynamics, based on a curated dataset of more than 150 Scopus documents. We begin with a bibliometric analysis mapping publication trends, influential authors, and thematic clusters. The review classifies car-following models and formally defines local, asymptotic, and string stability. Principal analytical methods, linear stability analysis, nonlinear perturbation methods, control-theoretic approaches, and simulation techniques are explained, emphasizing their roots in nonlinear science. We systematically examine homogeneous traffic stability, highlighting the roles of driver characteristics, time delays, and road geometry in shaping nonlinear wave dynamics. The major focus is mixed traffic, analysing how the penetration rate of connected automated vehicles, spatial distribution, platooning, communication imperfections, and human factors collectively influence the emergence and suppression of traffic waves. Critical research gaps are identified and future directions proposed, providing a comprehensive reference for researchers working at the intersection of nonlinear dynamics and next-generation transportation systems.

Mixed traffic flow composed of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) represents a core characteristic of intelligent transportation systems. However, its operational efficiency is significantly constrained by lane management strategies and CAV cooperative driving behaviors. To investigate this, a cellular automata-based simulation model is developed that integrates multiple car-following rules, a lane-changing strategy, and a platoon coordination mechanism. Through a systematic comparison of 13 lane management strategies in one-way two-lane and three-lane configurations, this study analyzes the influence mechanisms of lane allocation and cooperative driving on traffic flow, considering fundamental diagram characteristics, operating speed, CAV degradation behavior, and maximum platoon size. The results indicate that the performance of different strategies exhibits phased evolution with increasing CAV penetration rates. At low penetration rates, providing relatively independent space for HDVs effectively suppresses random disturbances and improves throughput. At medium to high penetration rates, dedicated CAV lanes—especially those with spatial continuity—enable cooperative platoons to fully leverage their advantages, leading to significant improvements in traffic capacity and operational stability. These findings demonstrate an optimal alignment between cooperative driving mechanisms and lane configurations, offering theoretical support for highway lane management in mixed traffic environments.

The spatial clustering of connected automated vehicles (CAVs), termed platoon intensity, can alter mixed traffic flow capacity and reduce traffic emissions, yet it remains inadequately understood compared to the widely studied CAV market penetration rate. This paper presents a systematic survey of 86 articles on platoon intensity in mixed traffic flow, identified through a structured Scopus search and analyzed using bibliometric methods. We establish a unified conceptual framework with formal definitions and estimation protocols for platoon intensity, size, strength, and organization, concepts that have been used inconsistently across the literature. Our synthesis reveals that platoon intensity consistently improves traffic capacity and stability, with the magnitude of effects increasing with CAV penetration rate, but that larger platoon sizes induce a fundamental trade-off between capacity gains and stability degradation, with optimal platoon sizes of 4–6 vehicles. Safety effects are non-monotonic: collision risk peaks at moderate CAV penetration rates (40–60%) regardless of platoon intensity, while platooning reduces fuel consumption by 18–25% at penetration rates above 60%. Three primary modeling paradigms dominate: Markov chains, car-following models, and cellular automata, each offering complementary strengths. We identify critical gaps in empirical validation, equity and social acceptance, and integration with multimodal transportation systems, and propose a concrete roadmap including data needs, inference challenges, and validation benchmarks using emerging field test data. This survey provides a comprehensive reference for researchers and practitioners managing CAV clustering effects in mixed traffic systems.

Vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) technologies enable connected and automated vehicles (CAVs) to drive in platoons and realize cooperative driving. To investigate the impact of CAVs and platoons on multi-lane mixed traffic flow, this paper proposes a multi-lane cellular automata model considering the self-organized strategy of CAV platoons, which can describe the LC behaviors of CAV platoons. First, different car-following (CF) modes are introduced, and the CF characteristics and the lane-changing (LC) motivations of vehicles in different CF modes are analyzed. Then, based on CF characteristics and LC motivations, CF rules and LC rules for vehicles in different CF modes are designed, and the multi-lane cellular automata model considering the self-organized strategy of CAV platoons is developed. Finally, the impact of the LC behaviors of CAV platoons on the mixed traffic flow is investigated based on the numerical simulation. Numerical simulation results showed that: (1) benefiting from the LC behaviors of CAVs, CAVs can significantly improve traffic capacity and average velocity in the two-lane scenario compared to the single-lane scenario. When the penetration rate (PR) is 80%, the capacity of one lane and average velocity in the twolane scenario increases by 14.3% and 41.4%, respectively, compared to the single-lane scenario. (2) under the same PR of CAVs, the LC frequency showed a tendency of increasing and then decreasing with the density increase. Besides, as the PR of CAVs increases, the LC frequency decreases. (3) CAVs are significantly more effective in alleviating traffic congestion at a low density than at a high density. (4) when the PR of CAVs is not higher than 40%, the maximum platoon size has almost no effect on traffic capacity and velocity. As the PR of CAVs increases, the effect of maximum platoon size gradually becomes significant.

To investigate the impact mechanism of different platoon control strategies on mixed traffic flow, this paper evaluates the overall performance of different heterogeneous platoon control strategies in smoothing small traffic disturbances and improving traffic safety. First, this paper derives the stability conditions for homogeneous and mixed traffic flow based on transfer function theory. Second, by simulating small disturbance experiments, the trend of speed under different traffic densities and the penetration rate of CAVs are analyzed. The characteristics of speed change coefficients under different platoon control strategies are comparatively analyzed based on the results in part 1. Finally, numerical simulation experiments were designed to analyze the safety performance of traffic flow under each strategy. The results show that (1) the combination of a variable time gap strategy with vehicle speed has the strongest ability to suppress disturbances. Among the combination spacing strategies, the combination of the variable time gap strategy with vehicle speed and the constant time gap strategy performs best in smoothing small disturbances. (2) At low penetration rates, incorporating CAVs may increase the instability of the traffic flow, while at high rates, CAVs effectively enhance the stability. These findings provide important guidance for selecting platoon control strategies in mixed traffic flow environments from the perspective of stability and safety.

As connected automated vehicle (CAV) technologies continue to develop rapidly, highways will remain in a mixed-traffic state where human-driven vehicles and CAVs coexist for a long time. Meanwhile, freight trucks account for a non-negligible proportion of highway traffic. Focusing on basic freeway segments, this paper proposes an analytical capacity model for mixed traffic flow that includes four vehicle classes. These classes are human-driven cars (HDCs), human-driven trucks (HDTs), connected automated cars (CACs), and connected automated trucks (CATs). Three key parameters are introduced: the car proportion alpha, the CAV penetration rate among cars beta, and the CAV penetration rate among trucks gamma. Based on these parameters, the proportions of each vehicle class in the mixed traffic are derived. Under the assumption of random mixing, eight car-following types are defined. Using the corresponding headway parameters, a weighted average headway is derived, and an analytical expression for roadway capacity is established. Systematic numerical experiments are conducted to examine how vehicle-class penetration rates, the truck proportion, and key headway parameters affect capacity. The results show that increasing the CAV penetration rate can significantly improve capacity. In particular, introducing CAT yields more pronounced capacity gains when the truck proportion is high. This study provides a theoretical basis for infrastructure planning and traffic management under mixed traffic conditions. The proposed model is intended for basic freeway segments under random vehicle mixing conditions.

Most self-driving laboratories (SDLs) rely on the assumption of a static and fully known experimental environment prior to execution, neglecting potential pre-experimental deviations and human errors that may trigger cascading failures during operation.To address these limitations, this paper presents a multi-agent SDL framework incorporating pre-execution environment perception. The system is designed to emulate human-like manipulation intelligence, integrating natural language interaction, synthesis planning, dexterous environment perception, cognitive task planning, and adaptive robot execution into a closed-loop workflow. The system is coordinated through six specialized agents-task clarification, synthesis planning, environment perception, robot planning, robot execution, and feedback-collectively enabling end-to-end automation from user intent to experimental realization. Validation was conducted on a customized automation platform using hydrogel and silicone preparation tasks, supported by a dedicated dataset encompassing these processes and multiple types of potential anomalies inconsistent with predefined conditions. Experimental results demonstrate high accuracy in both synthesis planning and environment perception (with perception accuracy reaching 99.86%), along with significant improvements in task success rates: hydrogel preparation increased from 0.64 to 0.90, and silicone preparation from 0.60 to 0.92. These findings confirm that integrating environment perception with multi-agent collaboration effectively enhances system robustness, safety, and adaptability under unexpected anomalies, underscoring the framework's potential for scalable deployment in real-world laboratory environments. Note to Practitioners-This work aims to develop an SDL capable of operating robustly under the imperfect conditions of real laboratory environments. We introduce a multi-agent framework that enables the SDL to perceive its surroundings before executing an experimental protocol. By integrating perception and planning agents, the system verifies whether the physical environment aligns with the operational requirements. It can also interact with users in natural language to clarify tasks and automatically identify anomalies that violate predefined conditions. We validated the effectiveness of this framework on a custom automated platform for hydrogel and silicone synthesis. The results show a substantial improvement in task success rate, translating directly into higher efficiency and reduced material consumption. Practically, embedding environmental perception and multi-agent verification into the experimental workflow enables the creation of autonomous systems that are not only more reliable and safer but also suitable for everyday research environments. The core architecture is platform-agnostic and can be applied to other laboratory automation and robotic manipulation tasks, particularly those where pre-execution validation is critical. In terms of system cost, based on our deployment experience, a setup equipped with basic pre-execution perception capabilities can be built for approximately USD 50,000-100,000. In terms of implementation effort, the deployment cycle from scratch to the first automated experiment typically ranges from 4 to 8 weeks, and can be even shorter for laboratories with existing automation infrastructure.

The integration of Large Language Models (LLMs) into intelligent transportation systems has opened a transformative paradigm for traffic signal control at urban intersections. To comprehensively understand this rapid evolution, this survey systematically examines 87 seminal studies retrieved from multiple mainstream databases following the PRISMA guidelines. As a core contribution, we propose a novel and unified taxonomy that categorizes current LLM-based approaches into three fundamental paradigms: LLM-augmented reinforcement learning, LLM-centric reasoning agents, and knowledge-enhanced hybrid systems. Core enabling technologies, including prompt engineering, parameter-efficient fine-tuning, multimodal perception, and edge deployment, are critically analyzed. Our systematic synthesis demonstrates that LLM-based controllers consistently outperform conventional and deep reinforcement learning baselines in optimizing traffic efficiency, exhibiting particularly robust generalization in out-of-distribution and emergency scenarios. Furthermore, we provide a domain-specific deep dive into the critical bottlenecks of deploying LLMs in safety-critical environments, specifically focusing on the severe physical consequences of model hallucination and the strict computational latency and API economic costs associated with real-time decision-making. Finally, we outline evidence-based future research directions toward embodied foundation models, neuro-symbolic safety architectures, and verifiable artificial intelligence. This survey delivers a structured, rigorous roadmap for researchers and practitioners aiming to advance LLMenabled intersection control toward safe, efficient, and cost-effective deployment.

In the first part of this study, different combination spacing control strategies for platoons are proposed. The combined performance of different strategies in terms of fuel consumption and emission is evaluated by numerical experiments. However, that study mainly focused on assessing the environmental benefits of the strategies and did not deeply explore the traffic flow characteristics under different spacing strategies. Therefore, the second part of this study focuses on analyzing the fundamental diagram of mixed traffic flow to explore further the effects of different control strategies on traffic flow characteristics. Firstly, the fundamental diagram models of mixed traffic flow under ten control strategies are established based on the three-parameter relationship of traffic flow and the definition of density. Subsequently, the theoretical fundamental diagrams are verified by numerical simulation experiments, and the effects of different control strategies on the average speed and capacity of mixed traffic flow are analyzed in detail. Finally, the sensitivity analysis of the free flow speed and the minimum safety spacing is carried out based on the fundamental diagram model. The results show that (1) introducing the CS strategy can significantly improve capacity, and the combination with the VTG strategy has the best effect. When the penetration rate of CAVs is 1, the maximum traffic capacity of the VTG1-CS strategy is close to 10,000 veh/h, which is more than 5 times that of the HV homogeneous traffic flow, while the enhancement of the CTG-CS strategy is about four times. (2) The maximum traffic capacity of the VTG2-CS strategy when the penetration rate of CAVs is 1 is about 110.69 veh/h less than that of the CTG-CS strategy, but it performs better in terms of overall traffic flow in traffic density. However, the BS-CS strategy is weaker in enhancing traffic capacity. (3) The VTG1-CS strategy is the most sensitive to free flow speed, followed by the CTG-CS strategy. In contrast, the VTG2-VTG2 strategy is the least sensitive to free flow speed, and the VTG2-CTG and VTG2-CS strategies based on this strategy are also less sensitive. Finally, the VTG2-VTG2 strategy and VTG2-CS strategy have the highest sensitivity to the minimum safety spacing parameter. To sum up, this study provides a theoretical basis for the mechanism of the effects on traffic flow characteristics with different spacing control strategies in mixed traffic flow.

This paper proposes an optimal design method for the adaptive cruise control model to enhance the string stability with the adaptive cruise control (ACC). First, the influence of control gain parameters on ACC and cooperative adaptive cruise control (CACC) systems is analyzed from theoretical and numerical perspectives. Second, we compared the ACC and CACC models. On this basis, an optimal control gain parameter is proposed to consider the string stability of the ACC platoon system. Finally, we designed numerical simulation experiments to verify the effectiveness of the proposed ACC (PACC) model. Results show that compared with the classical ACC model, the PACC model has certain advantages in recovery time, vehicle average velocity, velocity standard deviation, and vehicle collision safety. Moreover, PACC is suitable for most equilibrium velocity scenarios, and it has good string stability with different time gaps, unlike the ACC and CACC models. As a result, the PACC model has better string stability and robustness. Therefore, the PACC model can enhance the string stability and provide theoretical support for designing better ACC systems.

The contradiction between urban density and sustainable environmental development is increasingly prominent. Although numerous studies have examined the impact of urban density on air pollution at the macro level, most previous research at the micro scale has either neglected socioeconomic factors, failed to analyze heterogeneous effects, or ignored historic neighborhoods where high pollution coexists with high density. By considering population, commercial buildings, vegetation, and road factors, an integrated social-biophysical perspective was introduced to evaluate how urban density influences PM2.5 concentration in a historic neighborhood. The study area was divided into 56 units of 120 m × 150 m granularity, as determined by the precision of the LBS population data. The lasso regression and quantile regression were adopted to explore the main factors affecting PM2.5 and their heterogeneous effects. The results showed that (1) building density was the most important driving factor of pollutants. It had a strong and consistent negative effect on PM2.5 concentrations at all quantile levels, indicating the homogeneity effect. (2) Short-term human mobility represented by the visiting population density was the second main factor influencing pollutants, which has a significantly positive influence on PM2.5. The heterogeneous effects suggested that the areas with moderate pollution levels were the key areas to control PM2.5. (3) Vegetation Patch Shape Index was the third main factor, which has a positive influence on PM2.5, indicating the complex vegetation patterns are not conducive to PM2.5 dispersion in historic neighborhoods. Its heterogeneous effect presented a curvilinear trend, peaking at the 50th quantile, indicating that moderately polluted areas are the most responsive to improvements in vegetation morphology for PM2.5 reduction. These findings can provide effective support for the improvement of air quality in historical neighborhoods of the city’s central area.

Shared electric bikes (e-bikes) have become a rapidly growing mode of transportation worldwide, with electric bike-sharing systems (EBSSs) successfully implemented in numerous cities. The mainstream EBSSs can generally be categorized into two types: station-based and free-floating (or dockless). Each type has its respective advantages and disadvantages. For example, free-floating systems have a lower total construction and maintenance cost, but some users return e-bikes at improper locations without considering social impacts, such as blocking vehicle and pedestrian movements, and the induced safety issues. A hybrid e-bike sharing system (HEBSS) that combines elements from both systems has the potential to exploit the advantages of both and overcome their drawbacks, leading to an improvement in system performance. However, few existing studies have proposed a methodology to design such a system to demonstrate its effectiveness and address the inconsiderate e-bike return behavior. In this paper, we formulate the design problem of an HEBSS as a bi-level optimization problem. The upper-level problem is to determine the locations and capacities of various facilities, including charging stations and geofencing areas, aiming to maximize social welfare under a budget constraint. The lower-level problem is an e-bike sharing network equilibrium problem with elastic demand considering the inconsiderate drop-off behavior of users, waiting time costs, roaming behavior during rental and return processes, and parking rewards and fines. The upperlevel problem is solved by our proposed hybrid solution method, which is based on genetic algorithm coupled with our proposed capacity-setting heuristic. The lower-level problem is transformed into a fixed demand equivalent problem and solved by the self-regulated averaging method. We present numerical results to demonstrate the properties of the problem, identify the key factors that affect the design, illustrate the performance of the proposed hybrid solution algorithm, and provide design insights to the system operator.

To mitigate the impact of human-driven vehicles (HDVs) on connected and automated vehicles (CAVs) in mixed traffic environments, the implementation of dedicated lanes has been proposed to achieve partial separation between CAVs and HDVs, thereby improving the operational efficiency of both CAVs and the road segment. The lane management policy, where dedicated lanes for HDV (HDLs) and general lanes (GLs) coexist on a road segment, is referred to as the (G, H) policy. This paper proposes a multi-lane fundamental diagram model for mixed traffic flow and aims to investigate the effects of HDL configuration on the efficiency of road segments under the (G, H) policy. Firstly, different car-following modes in mixed traffic flow are analyzed, and various car-following models are employed to characterize the mixed traffic flow. Secondly, two lane selection principles are introduced to describe the lane choice behavior of HDVs under the (G, H) policy. Based on these principles, five equilibrium states that may exist on the road segment under the (G, H) policy are analyzed. Subsequently, a multi-lane fundamental diagram model incorporating HDL is derived based on the lane selection principles of HDV. Finally, numerical analysis is conducted to investigate the influence of lane configuration schemes under the (G, H) policy on the distribution of equilibrium states, fundamental diagram, and capacity. The results indicate that: (1) Based on the lane choice behavior of vehicles, the equilibrium states of road segment can be classified into five types. The distribution of each equilibrium state under different traffic conditions only depends on the proportion of HDL to the total number of lanes on road segment. A higher number of HDL leads to a reduced applicability of the (G, H) policy under different traffic conditions. (2) Under different penetration rates, as density increases, the overall traffic volume of road segment initially increases and then decreases until reaching the critical jammed density. (3) In a three-lane scenario, compared to the absence of HDL, the optimal HDL configuration scheme can increase the traffic volume of road segment by approximately 11%. (4) With HDL deployment, the capacity of road segment initially increases and then decreases with an increase in CAVs penetration rate.

Existing studies have demonstrated the potential of Connected and Automated Vehicles (CAVs) to optimise traffic flow and suppress disturbances. However, most current mixed traffic flow models adopt idealised deterministic approaches for modelling Human-Driven Vehicles (HDVs), and overlook the influence of the spatial distribution of CAVs on system performance. To address these limitations, this study introduces a novel metric (i.e., platoon intensity) to quantify the spatial clustering characteristics of CAVs within mixed traffic flow. This indicator enables a unified characterisation of CAV distribution patterns across various penetration rates, and theoretical bounds on pairwise vehicle probabilities under different traffic conditions are derived accordingly. A mixed traffic flow model is further developed, incorporating stochastic carfollowing behaviour of HDVs, behavioural degradation of CAVs, and a constraint on maximum platoon size. By introducing stochastic differential equations, the model successfully reproduces velocity fluctuations triggered by endogenous disturbances. Based on this framework, a series of systematic numerical experiments are conducted to comprehensively analyse traffic efficiency, stability, and energy consumption under varying CAV penetration rates and spatial distribution patterns. A quantitative relationship is established between platoon intensity and macroscopic traffic performance indicators. The main findings of this paper are as follows: (1) The spatial distribution of vehicles significantly impacts macroscopic traffic performance, with maximum differences of 9.70 %, 145.20 %, and 7.58 % observed in average speed, coefficient of variation of speed, and average energy consumption, respectively. (2) At a fixed CAV penetration rate, increasing platoon intensity enhances traffic efficiency and reduces average energy consumption, but exacerbates traffic instability. This research provides theoretical insights and practical implications for future CAV deployment strategies and traffic management measures.

connected and automated vehicles (CAVs) are a critical component of modern intelligent transportation systems, offering significant advantages in improving traffic efficiency, enhancing safety, and reducing energy consumption. Among these, CAV platoons have garnered considerable attention due to their ability to maintain stable spatial relationships and reduce time headway through cooperative control. information flow topology (IFT), as a core element of CAV platoon performance, determines the manner in which information is transmitted between vehicles. Although bidirectional IFT demonstrates strong stability advantages in single-vehicle control, its application to CAV platoons faces numerous challenges, and significant research gaps remain. To address this research problem, this article proposes a bidirectional distance-balancing strategy for CAV platoons, considering sensing and communication delays, which utilizes bidirectional spacing information to maintain vehicles in an equilibrium position between leading and following vehicles. Specifically, a constant spacing (CS) strategy is employed to control the following vehicles in the platoon, while a bidirectional information-based distance-balancing strategy is designed for the leading vehicle. Subsequently, numerical simulations are conducted in a mixed traffic flow environment to validate the effectiveness of the proposed strategy in terms of energy consumption, stability, and efficiency. The experimental results demonstrate that the proposed bidirectional distance-balancing strategy for CAV platoons exhibits excellent overall performance. Compared to single-vehicle balancing strategies, energy consumption is reduced by up to 40.81%, and the average travel speed is significantly improved. Compared to platoons without distance balancing, energy consumption is reduced by up to 4.99%, and the propagation of disturbances is better suppressed. This strategy provides a new approach for optimizing CAV platoon formation.

Compared with traditional vehicle longitudinal spacing control strategies, the combination spacing strategy can integrate the advantages of different spacing control strategies. However, the impact mechanism of different combination spacing control strategies on mixed traffic flow has not been analyzed yet. Therefore, this paper proposes various combination spacing control strategies for connected automated vehicles (CAVs). First, a mixed traffic flow model was developed to analyze the characteristics of CAV platoons. On this basis, a probability model of vehicle distribution was derived, and its effectiveness was verified through simulation. Then, multiple spacing combination strategies are proposed based on four spacing control strategies. Finally, numerical experiments were conducted to calculate the average fuel consumption and pollutant emissions of mixed traffic flow under different spacing control strategies, and the impact of platoon spacing control strategies and platoon size on traffic flow fuel consumption and pollutant emissions was further analyzed. Results show that: (1) the differences in average fuel consumption and pollutant emissions of traffic flow are relatively small under different platoon spacing control strategies under low traffic density (i.e., 5-20 veh/km); (2) at medium to high traffic densities (i.e., 40-120 veh/km), when the penetration rate of CAVs exceeds 0.8, VTG1-CS, VTG2-CS, and CTG-CS strategies can effectively ensure traffic flow stability and safety, and significantly reduce fuel consumption and pollutant emissions; (3) Only the BS-CS strategy has a higher sensitivity to platoon size for fuel consumption and emissions at low platoon sizes, and the rest of the strategies have a lower sensitivity to platoon size.

Multi-vehicle cooperative trajectory planning has emerged as a paradigm in connected autonomous vehicles (CAVs) technology research. One of the more common scenarios is cooperative lane-changing trajectory planning within CAVs and platoon coexistence environments. This research introduces a platoon-based cooperative lanechanging (PCLC) control strategy, facilitating a platoon optimally merge into another platoon. The strategy encompasses three dynamic traffic states: micro-platoon formation, lane-changing preparation, and platoon state recovery, which are integral to the lane-changing process. Two transition signals connect these states, enabling the smooth transition between car-following and lane-changing states for CAVs. The PCLC strategy is formulated mathematically through a hybrid model predictive control (MPC) system to achieve multiple objectives, including traffic smoothness, driving comfort, and terminal state reachability. The MPC model is optimized using receding horizon optimization, allowing the system to adapt to the dynamic traffic environment. Furthermore, the stability of the MPC system is proven theoretically. To validate the effectiveness of the proposed strategy, a collaborative simulation platform utilizing Python and SUMO has been established. The results show that (1) compared with the individual cooperative lane-changing (ICLC) strategy, this strategy can improve the lanechanging efficiency by 47.5%. (2) It becomes apparent that a positive speed difference between the subject CAVs and the target platoon will significantly affect the lane changing efficiency. In addition, the execution time is increased more than 30 % when the platoon size is more than 5 vehicles. (3) The application of greater weight to the acceleration penalty weight and the reduction of the vehicle's acceleration limitations can mitigate the speed fluctuations, thereby facilitating a smoother traffic flow. This study integrates CAV platooning control with lane changing control methods to improve lane changing efficiency and reduce traffic fluctuations. The findings of this paper will provide theoretical support for the application of CAV platoons.

The mixed traffic flow of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) will exist on highways for a long time, as the deployment of CAVs is gradual. To reduce the negative impact of HDVs on CAVs, the deployment of dedicated lanes has been considered an effective solution. Along with the dedicated lanes, three different lane management strategies will be formed, which are (C, H) strategy (CAVs dedicated lanes and HDVs dedicated lanes), (C, G) strategy (CAVs dedicated lanes and general lanes), and (G, H) strategy (general lanes and HDVs dedicated lanes). To evaluate the influence of dedicated lane settings on mixed traffic flow comprehensively, this paper proposes a framework for evaluating road segment efficiency and fuel consumption by considering lane management strategies. First, the possible traffic flow equilibrium states under three lane management strategies are discussed, and the characteristics of five car-following modes in mixed traffic flow are analyzed. Then, a mixed traffic flow capacity model considering platoon size is introduced to the traditional BPR function to establish a speed estimation model for mixed traffic flow and a fuel consumption estimation model for mixed traffic flow. Next, the traffic flow distribution model at the lane level in a steady state is derived for different lane management strategies. Based on the traffic flow distribution model, the speed estimation model and the fuel consumption estimation model for mixed traffic flow, which consider lane management strategies, are proposed. Finally, a numerical simulation is conducted to analyze the effects of different lane management strategies and configuration schemes on road segment efficiency and fuel consumption. The results of numerical experiments show that (1) at the same traffic demand, the operational speeds of vehicles under the (C, H) strategy and (G, H) strategy tend to increase and then decrease with the increase in the penetration rate of CAVs. While the speed of the vehicle under the (C, G) strategy increases with the increase in the penetration rate of CAVs. (2) Compared with the baseline strategy, all three management strategies can improve the operating efficiency of vehicles under certain traffic conditions. (3) At the same traffic demand, the average fuel consumption under the three strategies tends to decrease first and then increase slightly as the penetration rate increases. Increasing the number of dedicated lanes under specific traffic conditions can significantly increase the fuel consumption reduction rate under each strategy. At the same penetration rate, this advantage diminishes with the increase in traffic demand. (4) The increase in platoon size favors the efficiency of vehicle operations under different strategies. However, as platoon size increases, the marginal benefit of increasing platoon size becomes smaller and smaller. In addition, the average fuel consumption of vehicles has a low sensitivity to platoon size, and increasing platoon size may not always reduce fuel consumption.

To investigate how setting up connected and automated vehicles (CAVs) dedicated lanes can maximize the traffic capacity, this paper proposes a fundamental diagram of mixed traffic flow with CAVs dedicated lanes, in which CAVs dedicated lanes only allow CAVs and shared lanes allow both CAVs and human-driven vehicles (HDVs). Firstly, for CAVs overflowing and not overflowing, the car-following modes and their proportion in CAVs dedicated lanes and shared lanes are analyzed, respectively. Secondly, based on steady-state conditions and average time headway, the fundamental diagram of mixed traffic flow with and without CAVs dedicated lanes is derived. Then, the relevant properties of the fundamental diagram with CAVs dedicated lanes are proposed and proved. Finally, multiple parameters, such as the overflow ratio of CAVs, the penetration rate of CAVs, and the number of CAVs dedicated lanes, are adopted to discuss their impact on the fundamental diagram. Results show that (1) one and two CAVs dedicated lanes of three manual lanes can maximize the traffic capacity when the penetration rate of CAVs reaches 0.16 and 0.31, respectively; (2) compared with C-H policy, C-S policy can maximize the traffic capacity when the penetration rate of CAVs reaches 0.45 with one CAVs dedicated lane and 0.73 with two CAVs dedicated lanes; (3) the traffic capacity of C-S policy is up to 1.3 times and 1.6 times that of mixed traffic flow with one and two CAVs dedicated lanes of three manual lanes, respectively. This work provides insights into the impact of CAVs dedicated lanes on traffic systems and helps decide the optimal number of dedicated lanes.