Stability Of Traffic Flow Research Articles

Stability Analysis and Prediction of Traffic Flow of Trucks at Road Intersections Based on Heterogenous Optimal Velocity and Artificial Neural Network Model

The evolution of traffic-related accidents caused by long, short, and medium trucks at signalized road intersections have been underemphasized in the last few years. Far, little attention has been paid to the modelling of trucks traffic flow using an artificial neural network model and evaluating the stability analysis of trucks depending on the heterogenous optimal velocity. This research evaluates the effect of trucks on some specific traffic flow features. Over the years, it has been deduced that trucks, irrespective of their sizes, significantly impact their surrounding traffic flow due to their body sizes and operational features. In this study, we focused on modelling the traffic flow of trucks at signalized road intersections using traffic flow variables such as speed, traffic volume, traffic density, and time as our inputs and outputs. The truck traffic data was collected using up-to-date equipment such as video cameras and inductive loop detectors from the South Africa transportation network. During the ANN modelling of the truck traffic flow, we used 956 traffic datasets divided into 70% for training and 15% each for testing and validation. The ANN model results show testing regression values of R2 (0.99901). This shows that the inputs and output are well correlated and the ANN model’s superiority in predicting truck traffic flow at signalized road intersections. Based on the HEOV model results, the result of the research indicates that in the mixed traffic flow of trucks in real-life scenarios, the proportion of different trucks on the signalized road intersections rather than the proportions of types of trucks can be used in the determination of traffic flow stability of each truck. This research extends our knowledge of truck traffic flow modelling and provides a blueprint for examining the stability analysis of long, short, and medium trucks in their immediate driving environment.

Stability Analysis of Heterogeneous Traffic Flow with Connected and Automated Vehicles: Joint Consideration of Communication Failures and Driver Takeover

As a primary stage of connected and automated vehicles (CAVs), the cooperative adaptive cruise control (CACC) system takes some outstanding advantages such as accurate perception and timely reaction, which benefit from vehicular communication, over human-driven vehicles (HDVs). However, these advantages will turn into stability risks in case of communication failures caused by malicious attacks. Thus, this study aims at analyzing the stability of heterogeneous traffic flow with communication failures. We model two types of communication failures: bogus messages and transmission delay, and introduce drivers’ takeovers to react to communication failures. As a result, heterogeneous traffic flow consists of normal CACC vehicles, CACC vehicles with communication failures, and HDVs. Then, a series of numerical analyses, including startup and braking analysis, incidents analysis, and density wave, are proposed to verify the theoretical models and demonstrate their major properties. Besides, a discussion on traffic capacity is presented to analyze the overall impact of communication failures on traffic flow characteristics. The findings can help to investigate the stability evolution of heterogeneous traffic flow and determine the appropriate traffic flow configuration under communication failures.

Stability analysis of delayed-feedback control effect in the continuum traffic flow of autonomous vehicles without V2I communication

In this paper, a deterministic acceleration model is developed considering the sensor detection range to capture the underlying process of car following dynamic of autonomous vehicles. A delayed-feedback control is proposed using the difference between current state of throttle angle and previous one as feedback term and incorporated into the developed deterministic acceleration model to suppress the traffic congestion and increase the stability of traffic flow without the aid of Vehicle-to-Everything (V2I) communication. The feasible ranges of control parameters that ensure smooth traffic flow is derived from the perspective of the frequency domain via Hurvitz criteria and H∞ norm of transfer functions. The impact of proposed control strategy on the stability of macroscopic traffic flow is studied. The numerical-based simulations reveal that using the delayed-feedback of throttle angle with proper designed gains and delay time improves traffic efficiency, maintains scalable for large systems of autonomous vehicles, suppress the traffic congestion effectively, and dissipate shockwaves.

Stability and Resilience of Transportation Systems: Is a Traffic Jam About to Occur?

Measurement of traffic flow stability and resilience is a critical step toward evaluating the performance of transportation systems and implementing appropriate management strategies. Quantifying changes in the stability and resilience of transportation systems, however, is hampered by the complexity of real traffic dynamics and the diversity of infrastructures. Here, we demonstrate that changes in traffic flow stability and resilience are signaled by generic features, known as early warning signals in the theory of critical slowing down, observed before traffic instabilities occur. This finding is incorporated in an operational data-driven algorithm to evaluate the risk of traffic jams on highways. Theoretical findings and tests on simulated and empirical case studies support the premise of this approach and identify candidate statistical measures that are sensitive to changes in the stability and resilience of transportation systems. Our use of universal measures advances the monitoring capability, prediction and control of complex transportation systems.

Stability Analysis of the New Traffic Flow Lattice Model considering Taillight Effect and Speed Deviation

A new macroscopic traffic flow lattice model is established which considered the taillight effect and velocity deviation. By combining the concept of critical density, the critical condition of taillight triggering is established. The situation of taillight appearing is fully considered, and the influence of taillight effect is analyzed. Through the stability analysis of the model, the stability conditions of the model are obtained. By nonlinear analysis, the mKdV equation is derived which can describe the evolution mechanism of the density wave. From the phase diagram, we can find that the two factors have a positive impact on the stability of traffic flow. Finally, spatiotemporal evolution of the density wave and cross-section view of the density wave are obtained by numerical simulation to verify the theoretical derivation of this paper. The simulation results show that the stability of traffic flow can be improved by considering taillight effect and speed deviation. In addition, by comparing the time-space evolution maps of different parameter values, it can be found that the stability of traffic flow considering the taillight effect and speed deviation is better than that considering one factor alone. However, the stability of traffic flow will be negatively affected by low speed estimate and high critical density.

A model of lane-changing intention induced by deceleration frequency in an automatic driving environment

Achieving high-level, anthropomorphic lane-changing control for autonomous vehicles is an important method for promoting the safe and efficient operation of autonomous vehicles. According to existing lane-changing models, the important phenomenon that frequent acceleration and deceleration cause a poor riding experience and thus induce the intention of lane-changing needs to be given more attention. In this study, a discretionary lane-changing control method for autonomous vehicles is designed based on anti-interference ability (AIA, where AIA is the number of decelerations per unit time of an autonomous vehicle) to extract the traffic flow variation characteristics of autonomous vehicles with AIA and to determine methods to improve the driving efficiency and stability of autonomous vehicles. AIA is introduced to the lane-changing decision-making process of autonomous vehicles to simulate the phenomenon of lane-changing intention caused by poor riding experience. A control model that can evaluate the lane-changing conditions is then proposed. NetLogo software was selected to construct a one-way, two-lane scenario for autonomous vehicle operation, and the impacts of the proposed decision-making method in each scenario were tested. The experimental results show that autonomous vehicles are more likely to produce frequent deceleration behaviors under large-traffic and small-space operating conditions. A traffic flow composed of autonomous vehicles that use AIA to induce lane-changing intentions has a more obvious phase transition process. An autonomous vehicle whose AIA induces lane-changing intention can improve the speed and stability for a certain traffic volume (a saturation of less than approximately 0.45). Although lane-changing behavior breaks the stable state of two-lane traffic flow, it will also successfully suppress the aggravation of traffic congestion. Lane-changing has an impact on the local oscillation of traffic flow, and a stable traffic flow operation state and reduction in the AIA clearing frequency are mutually reinforcing.

Fundamental diagram and stability of mixed traffic flow considering platoon size and intensity of connected automated vehicles

The connected automated vehicle platoon is a critical organizational form of the intelligent transportation system in the future. Considering the platoon size and intensity of connected automated vehicles (CAVs), this paper studies the fundamental diagram and stability of mixed traffic flow. Firstly, the spatial distribution of vehicles in the mixed traffic flow is analyzed. Based on the platoon intensity and Markov chain, the proportion of different types of vehicles in the mixed traffic flow is deduced. Then, the general model framework of the fundamental diagram of the mixed traffic flow is established. The influence factors of the fundamental diagram under the critical scenarios are discussed. Finally, the general condition of mixed traffic flow stability considering the platoon size and intensity of CAVs is developed. Numerical experiments are adopted to analyze the influence of the characteristics of the CAVs platoon on stability. The results show that: (1) a greater platoon size leads to the increase of traffic capacity while it is harmful to the maintenance of traffic flow stability; (2) the platoon size is recommended to be set at 4 to 6 to balance the relationship between traffic capacity and stability; (3) a more significant platoon intensity can help improve the traffic capacity and stability; (4) the penetration rate of CAVs has a positive effect on the traffic flow stability until it increases to a certain degree.

Intelligent Traffic Flow Prediction and Analysis Based on Internet of Things and Big Data

Nowadays, the problem of road traffic safety cannot be ignored. Almost all major cities have problems such as poor traffic environment and low road efficiency. Large-scale and long-term traffic congestion occurs almost every day. Transportation has developed rapidly, and more and more advanced means of transportation have emerged. However, automobile is one of the main means of transportation for people to travel. In the world, there are serious traffic jams in almost all cities. The excessive traffic flow every day leads to the paralysis of the urban transportation system, which brings great inconvenience and impact to people's travel. Various countries have also actively taken corresponding measures, i.e., traffic diversion, number restriction, or expanding the scale of the road network, but these measures can bring little effect. Traditional intelligent traffic flow forecasting has some problems, such as low accuracy and delay. Aiming at this problem, this paper uses the model of the combination of Internet of Things and big data to apply and analyze its social benefits in intelligent traffic flow forecasting and analyzes its three-tier network architecture model, namely, perception layer, network layer, and application layer. Research and analyze the mode of combining cloud computing and edge computing. From the multiperspective linear discriminant analysis algorithm of the combination method of combining the same points and differences between data and data into multiple atomic services, intelligent traffic flow prediction based on the combination of Internet of Things and big data is performed. Through the monitoring and extraction of relevant traffic flow data, data analysis, processing and storage, and visual display, improve the accuracy and effectiveness and make it easier to improve the prediction accuracy of overall traffic flow. The traffic flow prediction of the system of Internet of Things and big data is given through the case experiment. The method proposed in this paper can be applied in intelligent transportation services and can predict the stability of transportation and traffic flow in real time so as to optimize traffic congestion, reduce manual intervention, and achieve the goal of intelligent traffic management.

Density waves in an improved car-following model under intelligent transportation system environment

The intelligent transportation system (ITS) can effectively utilize the existing transportation facilities and improve traffic safety and efficiency. To study and evaluate the dynamic characteristics of traffic flow, a novel car-following model is proposed based on the two-velocity difference model (TVDM) with connected consideration of headway memory and backward looking effect under ITS environment. The linear stability theory is used to acquire the stability condition of the improved model, which shows that the proposed model can effectively strengthen the traffic flow stability. According to the nonlinear theory, the time-dependent Ginzburg–Landau (TDGL) equation and modified Korteweg–de Vries (mKdV) equation are obtained to exhibit the evolution characteristics of traffic flow density wave. At last, numerical simulation is performed to further validate that the improved model can enhance the traffic flow stability and eliminate traffic congestion.

Stability analysis of heterogeneous traffic flow influenced by memory feedback control signal

Urban traffic is developing rapidly in the direction of intelligence and networking. In order to further enhance the role of connected vehicles in improving the traffic characteristic of the heterogeneous traffic flow, this paper designs a memory feedback control signal based on the historical traffic information of the vehicle itself, so as to improve the intelligent driver model. Furthermore, the improved homogeneous intelligent driver model is extended to heterogeneous intelligent driver model of heterogeneous traffic flow composed of connected vehicles and regular vehicles, and the stability of heterogeneous traffic flow is discussed. The stability condition of the heterogeneous traffic flow is derived by linear stability analysis method, which shows that the proportion and distribution of connected vehicles and the strength of memory feedback control signal affect the stability of traffic flow to a certain extent. In addition, the results of numerical simulation reveal that the stability of heterogeneous traffic flow is enhanced after considering the memory feedback control signal, no matter in low or high proportion of connected vehicles.

Integrated-Hybrid Framework for Connected and Autonomous Vehicles Microscopic Traffic Flow Modelling

In this study, a novel traffic flow modeling framework is proposed considering the impact of driving system and vehicle mechanical behavior as two different units on the traffic flow. To precisely model the behavior of Connected and Autonomous (CA) vehicles, three submodels are proposed as a novel microscopic traffic flow framework, named Integrated-Hybrid (IH) model. Focusing on the realization of the car following behavior of CA vehicles, the driving system (vehicle control system) and the vehicle mechanical system are modeled separately and linked by throttle and brake actuators model. The IH model constitutes the key part of the Full Velocity Difference (FVD) model considering the mechanical capability of vehicles and dynamic collision avoidance strategies to ensure the safety of following distance between two consecutive vehicles. Linear stability conditions are derived for each model and developing methodology for each submodel is discussed. Our simulations revealed that the IH model successfully generates velocity and acceleration profiles during car following maneuvers and throttle angle/brake information in connected vehicles environment can effectively improve traffic flow stability. The vehicles’ departure and arrival process while passing through a signal-lane with a traffic light considering the anticipation driving behavior and throttle angle/brake information of direct leading vehicle was explored. Our numerical results demonstrated that the IH model can capture the velocity fluctuations, delay times, and kinematic waves efficiently in traffic flow.

Static effect of assembled beam bridge under statistical and stable traffic flow load

The live load effect of bridge structure caused by traffic flow load is the largest in the sum of all live load effects. Study on the strain and deflection of the assembled beam bridge under the statistical stable traffic flow load can reveal the static effect and dynamic response law of the structure, and then evaluate the service performance of the structure. In this paper, the advanced bridge structure health monitoring technology is used to collect and analyze the traffic flow load parameters. First, according to the probability distribution function of each parameter in the traffic flow load model, the statistical stable traffic flow load is synthesized and the simplified bridge mechanical model is applied. Second, the variance and relative entropy are used to judge the statistical stability of the expectation, variance and probability density function of the static effect. Finally, the evolution law of the expectation, variance and probability density function of the static effect at different locations on the prefabricated beams of the whole bridge deck is analyzed when the static effect reaches the state of statistical stability.

Traffic flow control on cascaded roads by event‐triggered output feedback

AbstractIn this article, we propose an event‐triggered output‐feedback controller that guarantees the simultaneous stabilization of traffic flow on two connected roads. The density and velocity traffic dynamics are described with the linearized Aw‐Rascle‐Zhang macroscopic traffic partial differential equation model, which results in a coupled hyperbolic system. The control objective is to simultaneously stabilize the upstream and downstream traffic to a given spatially uniform constant steady‐state that is in the congested regime. To suppress stop‐and‐go traffic oscillations on the cascaded roads, we consider a ramp metering strategy that regulates the traffic flow rate entering from the on‐ramp to the mainline freeway. The ramp metering is located at the outlet with only boundary measurements of flow rate and velocity. Under the event‐triggered scheme, the control signal is only updated when an event triggering condition is satisfied. Compared with the continuous input signal, the event‐triggered boundary output control presents a more realistic setting to implement by ramp metering on a digital platform. The event‐triggered control design relies on the emulation of the backstepping boundary output feedback and on a dynamic event‐triggered strategy to determine the time instants at which the control value must be updated. We prove that there is a uniform minimal dwell‐time (independent of initial conditions), thus avoiding the Zeno phenomenon. We guarantee the exponential convergence of the closed‐loop system under the proposed event‐triggered control. A numerical example illustrates the results.

Analysis of drivers’ continuous delay time effect on the lattice hydrodynamic model with the on-ramp

In the real traffic, drivers often perceive traffic information with delay time, while presenting asynchronous reactions that form non-constant delay time. Meanwhile, on-ramps are very common in the real traffic network environment, especially on highway sections. Traffic flow merging from the on-ramps has a significant impact on the traffic stability of the main road. Given these facts, we develop an extended lattice hydrodynamic model with drivers’ continuous delay time and on-ramp. Based on the linear and nonlinear stability analysis, we derive the stability criterion and modified Korteweg–de Vries (mKdV) equation of the proposed lattice model; the kink-antikink soliton wave solution obtained by solving the above mKdV equation can be used to describe the propagation and evolution characteristics of density waves near the neutral stable curve. Results show that the ramp inflow ratio and delay time have a significant impact on the stability of traffic flow on the freeway main lane.

Modeling and stability analysis of traffic flow considering electronic throttle dynamics on a curved road with slope

To enhance traffic flow stability, the intricacy dynamic of road geometry characteristics on traffic safety is explored in this paper. And an extended macro traffic flow model is proposed coupled with electronic throttle (ET) dynamics effect for a single lane system. Based on the linear stability analysis, we can distinctly find that the stable region will varies due to the change of slope angle when the electronic throttle opening angle is fixed. Characteristics of density wave propagation near the neutral stability lines are described by means of the KdV equation, which is obtain from nonlinear analysis method. The new model also captures the evolution characteristics of shock and rarefaction waves. The simulation example verifies that the road geometric characteristics have a marked impact on the total energy consumption of the transportation system. The traffic congestion can be restrained efficiently by introducing the ET dynamics effect. The numerical simulation shows a good agreement with the analytical result.

Analysis of linear internal stability for mixed traffic flow of connected and automated vehicles considering multiple influencing factors

The stability of traffic flow reflects the ability to resist traffic perturbation and reveals the formation mechanism of congestion in a non-bottleneck link. To study the stability of mixed traffic flow in the non-fully connected and automated vehicles (CAVs) environment, this paper takes time delay, platooning intensity and CAVs degradations into consideration. Firstly, the vehicle types in the mixed traffic flow are analyzed considering degraded CAVs. Then time delays of different types of vehicles are discussed. The proportion of each type of vehicle under the influence of platooning intensity is determined. Based on this, the linear internal stability condition of mixed traffic flow is derived by using the characteristic equation-based method. The stability condition is related to CAVs degradations, time delay, and platooning intensity. In addition, the influence of CAV penetration rates, platooning intensity, and time delay on traffic stability are analyzed. Finally, the simulation experiments are carried out to verify theoretical results. The results show that: (1) CAV penetration rates and platooning intensity are conducive to maintaining the stability of the mixed traffic flow; (2) CAVs degradations and time delay have a negative effect on stability; (3) when the penetration rate of CAVs is more significant than 90.4%, the traffic flow is stable in the entire speed range. To a certain extent, these conclusions can enrich and expand the existing research results on the stability of the mixed traffic flow.

Dissipation Characteristics of Vehicle Queue in V2X Environment Based on Improved Car-Following Model

In recent years, the technology of Internet of Vehicles has developed rapidly, among which vehicle to everything (V2X) technology is provided with a quite important promotion value in the intelligent transportation field. In V2X environment, the driver can receive real-time information of the motion status of adjacent vehicles and the signal duration of the intersection, and take the decision-making action of manipulating the vehicle in advance, which has a certain impact on the evolution process of queue dissipation. This paper studies the improved OV model based on V2X environment and considering the driver’s early response time selects the intersection queue dissipation efficiency as the index to analyze the intersection traffic efficiency and studies the impact of different initial headway, maximum speed, and safety distance on the intersection traffic flow. The simulation results show that compared with the traditional OV model and the OV model considering driver’s reaction delay, the model proposed in this paper greatly improves the efficiency of traffic flow recovery after green light, so the model proposed in this paper can promote the stability of traffic flow. The linear stability of the proposed model was analyzed by using the linear stability theory to obtain the stability conditions of the improved model. Furthermore, a simulation environment was established to analyze the vehicle queue dissipation at intersections and to simulate the improved V2X car-following model as well as the platoon dissipation state under different traffic parameters. The research results show that vehicle drivers can obtain traffic flow operation status in real-time through V2X equipment and change the operation of the vehicle itself in a targeted manner, which can significantly improve the safety and stability of traffic flow operation, and greatly raise the dissipation efficiency of the platoon. The method proposed in this paper can increase the queue dissipation rate and shorten the recovery time of the traffic system, respectively.

Flow-level coordination of connected and autonomous vehicles in multilane freeway ramp merging areas

On-ramp merging areas are deemed to be typical bottlenecks for freeway networks due to the intensive disturbances induced by the frequent merging, weaving, and lane-changing behaviors. The Connected and Autonomous Vehicles (CAVs), benefited from their capabilities of real-time communication and precise motion control, hold an opportunity to promote ramp merging operation through enhanced cooperation. The existing CAV cooperation strategies are mainly designed for single-lane freeways, although multilane configurations are more prevailing in the real-world. In this paper, we present a flow-level CAV coordination strategy to facilitate merging operation in multilane freeways. The coordination integrates lane-change rules between mainstream lanes, proactive creation of large merging gaps, and platooning of ramp vehicles for enhanced benefits in traffic flow stability and efficiency. The strategy is formulated under an optimization framework, where the optimal control plan is determined based on real-time traffic conditions. The impacts of tunable model parameters on the produced control plan are discussed in detail. The efficiency of the proposed multilane strategy is demonstrated in a micro-simulation environment. The results show that the coordination can substantially improve the overall ramp merging efficiency and prevent recurrent traffic congestions, especially under high traffic volume conditions.

Visualization and Analysis of Mapping Knowledge Domain of Heterogeneous Traffic Flow.

Mapping knowledge domain (MKD) is an important application in bibliometrics, which is a method of visually presenting and explaining newly developed interdisciplinary scientific fields using data mining, information analysis, scientific measurement, and graphic rendering. This study combines applied mathematics, visual analysis technology, information science, and scientometrics to systematically analyze the development status, research distribution, and future trend of the heterogeneous traffic flow by using the MKD software tools VOSviewer and CiteSpace. Based on the MKD and Bibliometrics approaches, 4709 articles have been studied, which were published by Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) from 2004 to 2021 in the field of heterogeneous traffic flows. Firstly, this paper presents the annual numbers of articles, origin countries, main research organizations, and groups as well as the source journals on heterogeneous traffic flow studies. Then, cocitation analysis is used to divide heterogeneous traffic flow into three main research directions, which include “heterogeneous traffic flow model,” “traffic flow capacity analysis,” and “traffic flow stability analysis.” The keyword cooccurrence analysis is applied to identify five dominant clusters: “modeling and optimization methods,” “traffic flow characteristics analysis,” “driving behavior analysis,” “simulation experiment,” and “policies and barriers.” Finally, burst keywords were studied according to the publication date to present more clearly the change of research focus and direction over time.

Advanced traffic congestion early warning system based on traffic flow forecasting and extenics evaluation

Traffic congestion is a vital factor hindering travel. As such, developing a reliable traffic congestion early warning system is essential for providing traffic condition supervision and programming. However, previous research has rarely focused on traffic flow characteristics or on providing comprehensive assessments, resulting in poor warning performances. In this study, an innovative traffic congestion early warning system is proposed, comprising point forecasting, characteristic estimate, interval prediction, and comprehensive assessment. In the characteristic assessment phase, eight common statistical distributions are used to fit the characteristics of an original traffic flow parameter series in a training set, and the best fitting results are considered as the basis for building a prediction interval. An extreme learning machine combined with a modified multi-objective dragonfly optimization algorithm and variational mode decomposition is constructed in the point forecasting phase to provide accurate and stable traffic flow parameter forecasting results; two different strategies are used to establish the prediction interval, so as to conduct interval forecasting based on different types of uncertainty information (probability distribution information or known interval information). Extenics evaluation theory is then used in the comprehensive assessment phase to evaluate the traffic congestion level. Simulations of traffic flow parameter series, including simulations of the road density, road occupancy, and average velocity, reveal that the proposed early warning system demonstrates powerful abilities based on its precision and stability. The mean absolute percentage error (MAPE) values of the traffic flow parameters for the three datasets are 3.6265%, 3.7203%, and 4.5100%, respectively. The forecasting accuracy for the traffic congestion level is more than 97% for both point and interval prediction. Thus, this approach can be widely used for personal traffic route planning and the unified management of governmental traffic conditions.