During the proliferation process of Intelligent Connected Vehicles (ICVs), mixed traffic consisting of Human-Driven Vehicles (HDVs) and ICVs is inevitable. Notably, varying trust attitudes of HDV drivers towards ICVs lead to diverse driving behaviors when they interact with ICVs. The impact of these differential driving behaviors on the operational characteristics of the mixed traffic flow is currently unclear. This study focuses on examining the capacity and stability of ICV mixed traffic flow in a single-lane, no-overtaking scenario. We investigate the car-following behaviors of HDV drivers with different trust attitudes towards ICVs. From this, we discern the expected distribution probabilities of vehicles exhibiting varying behaviors in the mixed flow. Utilizing these findings, a theoretical model has been developed to analyze the fundamental diagram and stability of mixed traffic flow, considering the trust attitudes of HDV drivers toward ICVs. Through numerical analysis and simulation experiments, the impact of HDV drivers' trust attitudes in ICVs on the capacity and stability of mixed traffic flow was examined. The results show that ICV integration can bolster traffic flow capacity. Further, heightened trust attitude in ICVs among HDV drivers magnifies the positive effect of ICVs on traffic operational efficiency. However, if the ICV penetration rate remains below 70% and the trust level of HDV drivers towards ICVs does not reach a critical threshold, the integration of ICVs could potentially reduce traffic stability. Conversely, once the ICV penetration rate surpasses 70%, a simultaneous increase in ICV penetration rate and the trusting attitude of HDV drivers towards ICVs significantly improves the stability of mixed traffic flow. 

Modeling and Analysis of Mixed Traffic Flow Capacity and Stability Considering Human-Driven Vehicle Drivers' Trust Attitude towards Intelligent Connected Vehicles

Car-following behavior is influenced by multiple factors. Utilizing the potential field theory, previously determined as a comprehensive representation of these influencing factors, this study introduces a unified potential field-based car-following model tailored for mixed traffic flow. Considering the diverse information receptivity of various vehicle types, the model is shaped to encapsulate both Human Driven Vehicles and Connected and Automated Vehicles. Using the characteristic equation-based method, the stability conditions of the model are delineated. Subsequently, a detailed examination of mixed traffic flow stability was conducted across varying Connected and Automated Vehicles penetration rates. Leveraging a dataset of 300 real-world trajectories, the results were benchmarked against classical models such as the Intelligent Driver Model. Notably, our proposed model exhibited an 18.67 % improvement in safety metrics and a 1.36 % improvement in fuel economy over the Intelligent Driver Model when representing Connected and Automated Vehicles. The stability of the mixed traffic flow was found to correlate strongly with vehicular time delay and acceleration parameter weights. Furthermore, augmenting the Connected and Automated Vehicles penetration rate surfaced as a promising strategy to bolster traffic flow stability. Collectively, these findings furnish substantial enhancements to the existing body of knowledge on mixed traffic flow stability, offering pragmatic insights for future research and real-world applications, including traffic management, urban planning, and the development of Intelligent Transport Systems. 

Potential field-based modeling and stability analysis of heterogeneous traffic flow

/pdf/identifying-influential-users-in-unknown-social-networks-for-1lk4990r.pdf

Identifying influential users in unknown social networks for adaptive incentive allocation under budget restriction

The study of road traffic flow theory utilizes physics and applied mathematics to analyze relevant parameters and their relationships quanlitatively and quantitatively, in order to explore their dynamic changes. The fluid dynamics model used for traffic flow analysis is highly favored by scholars due to its solid mathematical foundation and good simulation results. However, existing models have two main shortcomings: firstly, existing research is mostly limited to non-viscoelastic fluid equation or incompressible non-Newtonian fluid equation, making it difficult to accurately describe the viscosity state and micro cluster properties of the actual traffic flow; secondly, the existing non-Newtonian fluid partial differential equations (PDEs) rely heavily on the finite element method (FEM) for solving, requiring higher computational cost, larger storage space, and more constraint conditions. Thus, in this paper, a traffic flow equation based on compressible non-Newtonian fluid has been constructed, and it has been solved by using physical-informed rational neural network (PIRNN) and noise heavy-ball acceleration gradient descent (NHAGD) to ensure learning and training speed and accuracy. Numerical results indicate that the proposed method can truly reflect the gradual change process in the viscosity of traffic flow, and has better solving performance than traditional FEM and physical-informed neural network (PINN) with activation functions; under the same conditions, the prediction error of the proposed method is also smaller than that of traditional traffic flow models. 

Compressible Non-Newtonian Fluid Based Road Traffic Flow Equation Solved by Physical-Informed Rational Neural Network

Most of the research on driving stability control of distributed drive electric vehicles is based on a yaw motion design controller. The designed controller can improve the lateral stability of the vehicle well but rarely mentions its changes to the roll and pitch motion of the body, and the uneven distribution of the driving force will also cause instability in the vehicle speed, resulting in wheel transition slip, wheel sideslip, and vehicle stability loss. In order to improve the spatial stability of distributed-driven electric vehicles and resolve the control instability caused by their motion coupling, a decoupled control strategy of yaw, roll, and pitch motion based on multi-objective constraints was proposed. The strategy adopts hierarchical control logic. At the upper level, a yaw motion controller based on robust model predictive control, a roll motion controller, and a pitch motion controller based on feedback optimal control are designed. In the lower level, through the motion coupling analysis of the vehicle yaw control process, based on the coupling analysis, the vehicle yaw, roll, and pitch decoupling controller based on multi-objective constraints is designed. Finally, the effectiveness of the decoupling controller is verified.

Decoupling Control of Yaw Stability of Distributed Drive Electric Vehicles

With the popularization of autonomous vehicle (AV) technology, mixed traffic flows that consist of AVs and human-driven vehicles (HDVs) will appear in the real world. Although many studies of the features of mixed traffic flow have been carefully evaluated, few studies have focused on the effect of aggressive driving performance on mixed traffic flow. This study aims to develop an approach to evaluate the effects of aggressive driving on the stability and capacity performance under the conditions of AV and HDV mixed traffic flow. First, since a car-following model can describe the relationship between vehicles, we calibrate a car-following model for aggressive driving and nonaggressive driving behaviors based on real traffic data and previous research results. Then, in a mixed traffic flow environment, a basic linear stability formula and capacity calculation expression are developed that consider the effects of vehicle order on the capacity. Finally, because the proportion of aggressive driving and aggressive driving parameters may change, nine combinations of three aggressive driving proportions and three driving parameter cases are used for the sensitivity analysis. The results indicate that the effect of aggressive driving on mixed traffic flow is complex. When the proportion of aggressive driving is less than 35%, the increase in the proportion of aggressive driving increases the traffic capacity and reduces the unstable part. However, when the proportion of aggressive driving is greater than 35%, the increase in the proportion of aggressive driving increases the unstable part. When the penetration rate of AVs exceeds 0.490, mixed traffic flow remains stable at all aggressive driving proportions. In addition, the capacity of a mixed traffic flow may be improved as the penetration rate of AVs increases. To a certain extent, these conclusions provide a theoretical basis for formulating different management modes of AVs and HDVs.

/pdf/exploring-the-stability-and-capacity-characteristics-of-c8s7tdrs.pdf

Exploring the Stability and Capacity Characteristics of Mixed Traffic Flow with Autonomous and Human-Driven Vehicles considering Aggressive Driving

In online influence maximization, a learner aims to find a specified number of nodes that have the greatest influence in a network, by iteratively selecting seed nodes (i.e., initially activated nodes) and updating its knowledge of the network via activation feedback. Existing approaches to this problem customarily assume that the structure of the network is known in advance, and focus on how to utilize activation feedback to reveal the features of seed nodes in each iteration, regardless of non-seed nodes which occupy the majority of the node set. In this paper, we present a novel learning framework to carry out online influence maximization in the absence of network structure. In our framework, the underlying influence relationships between nodes are inferred based on activation feedback, and then the influence reachabilities of both seed nodes and non-seed nodes are updated with the latest inferred influence relationships, so that more knowledge about the network can be used to guide the selection of seed nodes in the next iteration. Extensive experiments on both synthetic and real-world networks are conducted, and the results verify the efficacy of our proposed framework. 

Online influence maximization in the absence of network structure

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Exploring the Stability and Capacity Characteristics of Mixed Traffic Flow with Autonomous and Human-Driven Vehicles considering Aggressive Driving

Online influence maximization in the absence of network structure