Stability Of Traffic Flow Research Articles

Impact of driver's advanced reaction time in car-following model with optimal velocity deviation under V2X environment

The vehicle-to-everything (V2X) technology has made a significant advancement in the field of intelligent transportation in recent years. When operating a vehicle in a V2X environment, the driver can get real-time updates on the motion of nearby vehicles. In a real-world traffic situation, there is always some deviation between the actual and anticipated traffic information, and this deviation will undoubtedly have a significant effect on traffic flow. Drivers always maintain the flow of traffic by taking some time to assess and decide how the drivers in front of them are behaving behind the car in the traffic system. As a result, by taking into account the driver's advanced reaction time and optimal deviation in a V2X environment, a novel car-following model is developed and the effects of these parameters on the traffic flow are examined. In order to determine the stability requirements for the new model, the linear and nonlinear stability of the proposed model is examined using the perturbation methods. Studies have found that when the driver's reaction time and the velocity deviation are optimized, it can reduce the amplitude of the stability curve, thus enlarging the area of more stable motion. Also, the numerical simulation supports the theoretical research by showing that the new model may effectively reduce traffic congestion and improve the stability of traffic flow as the influence of these factors on the traffic flow increases.

Heterogeneous lattice hydrodynamic model and jamming transition mixed with connected vehicles and human-driven vehicles

As communication and perception technologies develop, connected vehicles (CVs) advance rapidly. In the process of popularization of CVs, it is bound to lead to the coexistence of CVs and human-driven vehicles (HDVs) on the road network for some time to come, and the previous study assumes that road vehicle is homogeneous, which could not quantify the difference between mixed vehicular flow, based on this, we consider the differences between CAVs and CVs in the way of information acquisition, a heterogeneous lattice hydrodynamics model mixing the HDVs and CVs are presented. Subsequently, by using the small perturbation method, we analyze the linear stability of the proposed model and derive the corresponding stability criteria; when the above stability norm does not hold, in order to investigate the nonlinear phenomenon, nonlinear stability analysis is performed and a modified Korteweg–de Vries equation (mKdV) corresponding to the proposed model is derived. By solving the mKdV equation, we obtain the kink - antikink solitary wave solution which can be used to explain how traffic jams form and propagate. At last, several numerical simulations were conducted to test the impact of the penetration rate of CVs and the bi-directional visual field for both forward-looking and backward-looking on the heterogeneous traffic flow stability, i.e., the first two are positive, while the last is negligible, this conclusion is consistent with the conclusion of theoretical derivation.

Analysis and improvement of car-following stability for connected automated vehicles with multiple information uncertainties

Connected automated vehicles have the capability to operate autonomously by monitoring real-time traffic information through on-board sensors, such as velocity and distance. However, no measurement can be perfect, and sensors are no exception, especially in challenging road and weather conditions, leading to the deviations between multiple information measured by vehicles and true information. Since the sizes of sensor detection errors are uncertain, we call this issue as multiple information uncertainties. This issue affects not only the normal operation of host vehicle directly but also the connected automated vehicular flow indirectly through wireless communication, resulting in the instability of car-following behavior and further deteriorating traffic congestion. So far, it is hard for us to obtain the repeatable, transferable, and even comparable results due to the lack of generic model. Therefore, this study develops a generalized model by using the uncertainty levels of multiple information to describe the dynamics of connected automated vehicles under the influence of sensor detection errors based on car-following theory. The theoretical and simulation-based investigations present a complete method to analyze the stability of traffic flow under multiple information uncertainties. Analytical results show that traffic stability will be reduced when the velocity measured by sensors is smaller than true velocity (i.e., negative uncertainty level of velocity information) or the headway monitored by sensors is bigger than real headway (i.e., positive uncertainty level of headway information), whereas the velocity and headway of equilibrium state will be enlarged. Otherwise, the opposite. These findings indicate that the impacts of multiple information uncertainties are double-edged swords, depending on the uncertainty levels of different information. To improve the adverse impacts of multiple information uncertainties on traffic stability, this study proposes a novel car-following controller and verifies its effectiveness. Overall, the present study provides a set of theoretical frameworks to investigate and improve traffic stability under multiple information uncertainties. All results contribute to enhancing the stability of traffic flow and further easing traffic congestion.

Prediction of Freeway Traffic Breakdown Using Artificial Neural Networks

Traffic breakdown is the transition of traffic flow from an uncongested state to a congested state. During peak hours, when a large number of on-ramp vehicles merge with mainline traffic, it can cause a significant drop in speed and subsequently lead to traffic breakdown. Therefore, ramp meters have been used to regulate the traffic flow from the ramps to maintain stable traffic flow on the mainline. However, existing traffic breakdown prediction models do not consider on-ramp traffic flow. In this paper, an algorithm based on artificial neural networks (ANN) is developed to predict the probability of a traffic breakdown occurrence on freeway segments with merging traffic, considering temporal and spatial correlations of the traffic conditions from the location of interest, the ramp, and the upstream and downstream segments. The feature selection analysis reveals that the traffic condition of the ramps has a significant impact on the occurrence of traffic breakdown on the mainline. Therefore, the traffic flow characteristics of the on-ramp, along with other significant features, are used to build the ANN model. The proposed ANN algorithm can predict the occurrence of traffic breakdowns on freeway segments with merging traffic with an accuracy of 96%. Furthermore, the model has been deployed at a different location, which yields a predictive accuracy of 97%. In traffic operations, the high probability of the occurrence of a traffic breakdown can be used as a trigger for the ramp meters.

Enhanced traffic safety and efficiency of an accelerated LC decision via DNN-APF technique

The decision-making module that operates in real-time is extensively valued for promoting traffic flow stability and throughput during autonomous driving. However, it may become problematic for autonomous vehicles to conduct Lane-changing (LC) decisions for acceleration in challenging urban scenarios, which poses certain risks to driving safety. This study develops a Deep Neural Network (DNN) and Artificial Potential Field (APF) model with appropriate feature selection for safe and efficient accelerating LC decisions. Firstly, a two-lane driving scenario with 5+1 vehicles is established. In the course of accelerating LC, 7 analysis methods are exploited to identify areas of vehicle concern with consideration of 30 different features. Secondly, the methods of DNN, Support Vector Machine (SVM), and K-nearest Neighbor (KNN) that commonly used in the prediction of decision classification are analysed and compared, based on which the DNN model is selected and obtained with an optimal decision effect and potential improvement extent. Furthermore, taking into account the security considerations of LC decision and the possible improvement of DNN model, the risk monitoring method of APF with extensive application background is introduced into the DNN results. Finally, the proposed DNN-APF approach is compared with pure DNN in terms of fitting degree, safety factor, and velocity enhancement efficiency. The results indicate that under the premise of reasonable feature extraction, the DNN-APF approach achieves the accelerated LC behaviour decision with high accuracy and safety performance in comparison with SVM, KNN, and the DNN without security considerations.

The jamming transition of multi-lane lattice hydrodynamic model with passing effect

Multi-lane environments are not uncommon on the highway. In multi-lane scenarios, vehicles can not only switch freely between lanes but also overtake each other. However, as basic driving behavior, the passing effect under multi-lane environments has been neglected in traffic flow research. To fill this gap, we present a modified multi-lane lattice hydrodynamic model considering the passing effect. Based on the reduction perturbation method, the corresponding stability norm of the proposed model is obtained, which reveals that the total number of lanes is positively correlated with the stability of traffic flow. When the stability condition does not hold, to further examine the formation and transmission process of traffic jams near the neutral stability curve, we carry out a nonlinear stability analysis of the model. The modified Korteweg-de Vries (mKdV) equation and the existence of the above mKdV equation are deduced, respectively. When the passing ratio is sufficiently small, i.e., the above existence holds, we observe that the jamming transition emerges between uniform flow and kink jam; once the passing ratio exceeds the threshold, the jamming transition occurs among uniform flow and kink-Bando traffic wave through the chaotic phase. We also conduct numerical simulations to verify the theoretical derivation.

A Collaborative Merging Method for Connected and Automated Vehicle Platoons in a Freeway Merging Area with Considerations for Safety and Efficiency.

To solve the problems of congestion and accident risk when multiple vehicles merge into the merging area of a freeway, a platoon split collaborative merging (PSCM) method was proposed for an on-ramp connected and automated vehicle (CAV) platoon under a mixed traffic environment composed of human-driving vehicles (HDV) and CAVs. The PSCM method mainly includes two parts: merging vehicle motion control and merging effect evaluation. Firstly, the collision avoidance constraints of merging vehicles were analyzed, and on this basis, a following-merging motion rule was proposed. Then, considering the feasibility of and constraints on the stability of traffic flow during merging, a performance measurement function with safety and merging efficiency as optimization objectives was established to screen for the optimal splitting strategy. Simulation experiments under traffic demand of 1500 pcu/h/lane and CAV ratios of 30%, 50%, and 70% were conducted respectively. It was shown that under the 50% CAV ratio, the average travel time of the on-ramp CAV platoon was reduced by 50.7% under the optimal platoon split strategy compared with the no-split control strategy. In addition, the average travel time of main road vehicles was reduced by 27.9%. Thus, the proposed PSCM method is suitable for the merging control of on-ramp CAV platoons under the condition of heavy main road traffic demand.

Bifurcation Analysis of Improved Traffic Flow Model on Curved Road

Abstract Nonlinear analysis of complex traffic flow systems can provide a deep understanding of the causes of various traffic phenomena and reduce traffic congestion, and bifurcation analysis is a powerful method for it. In this paper, based on the improved Aw–Rascle model, a new macroscopic traffic flow model is proposed, which takes into account the road factors and driver psychological factor in the curve environment which can effectively simulate many realistic traffic phenomena on curves. The macroscopic traffic flow model on curved road is analyzed by bifurcation, first, it is transformed into a nonlinear dynamical system, then its stability conditions and the existence conditions of bifurcations are derived, and the changes of trajectories near the equilibrium points are described by phase plane. From an equilibrium point, various bifurcation structures describing the nonlinear traffic flow are obtained. In this paper, the influence of different bifurcations on traffic flow is analyzed, and the causes of special traffic phenomena such as stop-and-go and traffic clustering are described using Hopf bifurcation as the starting point of density temporal evolution. The derivation and simulation show that both road factors and driver psychological factor affect the stability of traffic flow on curves, and the study of bifurcation in the curved traffic flow model provides decision support for traffic management.

Platoon Intensity of Connected Automated Vehicles: Definition, Formulas, Examples, and Applications

The strength of connected automated vehicles (CAVs) clustering, namely, platoon intensity, has an essential impact on the traffic capacity, safety, and stability of the mixed traffic flow with CAVs and human-driven vehicles (HDVs). This article proposes the definition, formulas, examples, analysis, and applications of CAV platoon intensity. Firstly, the CAV platoon intensity considering the maximum platoon size is defined, and its mathematical calculation method is developed. Secondly, the three types of value ranges for platoon intensity are discussed. Thirdly, the distribution characteristics of five types of time headways were proposed. Finally, the influence of CAV platoon intensity on the fundamental diagram and traffic safety of mixed traffic flow are analyzed from theoretical derivation and simulation evaluation, respectively. The result suggests the following: (1) According to the sensitivity analysis, an optimal platoon size of 5 vehicles was suggested for the CAV platoon; (2) The penetration rates and platoon intensity of CAVs can improve the capacity of mixed traffic flow, and the increase is gradually more significant; (3) The effect of the increase in platoon intensity on the capacity is not significant enough when the CAV penetration rate is low; (4) A higher platoon intensity is good for dissipating disturbances in the mixed traffic flow; and (5) CAV platoon intensity has a marginal impact on the characteristics of mixed traffic flow with the low penetration rate. It is noteworthy that a reasonable increase in platoon intensity can improve the traffic capacity and safety of mixed traffic flow.

Application of conditional generative adversarial network to multi-step car-following modeling.

Car-following modeling is essential in the longitudinal control for connected and autonomous vehicles (CAVs). Considering the advantage of the generative adversarial network (GAN) in capturing realistic data distribution, this paper applies conditional GAN (CGAN) to car-following modeling. The generator is elaborately designed with a sequence-to-sequence structure to reflect the decision-making process of human driving behavior. The proposed model is trained and tested based on the empirical dataset, and it is compared with a supervised learning model and a mathematical model. Numerical simulations are conducted to verify the model's performance, especially in the condition of mixed traffic flow. The comparison result shows that the CGAN model outperforms others in trajectory reproduction, indicating it can effectively imitate human driving behavior. The simulation results suggest that the introduction of CGAN-based CAVs improves the stability and efficiency of the mixed traffic flow.

Stability Analysis of Mixed Traffic Flow Considering Personal Space under the Connected and Automated Environment

A traffic survey using an unmanned aerial vehicle on the Haixia road in Chongqing, China found a significant correlation between the velocity of the vehicle and the distance of the vehicle when moving forward and laterally. To mitigate driving disruptions owing to vehicle intrusion at a desired distance, personal space (PS) is introduced to analyze the car-following behavior of continuous mixed traffic flow formed by human-driven (HD) vehicles and cooperative adaptive cruise control (CACC) vehicles. PS is a virtual boundary that refers to the space where psychological tension is caused by the invasion of others. Further, an intelligent driver model (IDM) was used to establish a mixed traffic car-following PS-IDM. The stability of the disturbance transfer function analytical model in homogeneous and heterogeneous traffic flows was used to calculate the traffic flow stability region under different CACC vehicle permutations. The results show that the PS-IDM-based car-following model effectively improves the stability fitting effect of mixed traffic flow, and the driving comfort is increased by up to 20.7% when compared with that of the single car-following model. In addition, there is a negative correlation between the PS and the unstable velocity range of the traffic flow. Compared with the homogeneous HD traffic flow, the average intrusion rate of homogeneous CACC traffic flow is reduced by 4.6%, and the driving comfort of vehicles is improved by approximately 65%.

A mixed capacity analysis and lane management model considering platoon size and intensity of CAVs

Traffic flow will be mixed with connected automated vehicles (CAVs) and human-driven vehicles (HDVs) in the future. The randomness of the spatial distribution of different types of vehicles (i.e., CAVs and HDVs) will not be conducive to the stability and safety of traffic flow, leading to the deterioration of traffic capacity. Therefore, reasonable organization and management of the spatial distribution of vehicles in mixed traffic flow are significant for improving the performance of transportation systems. To effectively organize CAVs and realize the management of automated dedicated lanes, this paper proposes a mixed capacity and lane management model considering platoon size and intensity of CAVs. Firstly, the spatial distribution of different headway types is calculated based on a Markov chain model. Secondly, a single-lane capacity model is developed based on the headway distribution. Then, we analyze the sensibility of the model’s parameters, including market penetration rates, platooning intensity, and platoon size of CAVs. Finally, we investigate the relationship between traffic capacity and lane management. Numerical analyses illustrate that the single-lane capacity is improved by increasing the market penetration rate, platoon size, and platooning intensity of CAVs. Moreover, The insight of the lane management model indicates that optimal lane management is associated with the market penetration rate of CAVs. These findings provide a strategy for the operation and management of dedicated lanes of CAVs in the future.

An Application of the Node–Place Model to Explore the Land Use–Transport Development Dynamics of the I-287 Corridor

A sustainable development plan should identify future urban development sites to maintain a balanced condition between transportation systems and land use. Most commonly used for Transit Oriented Development (TOD), the node–place model checks the balance between transportation systems and land use. While previous node–place research focused mostly on rail transportation, this research focuses on highway accessibility to assess future growth and urban development. To gain insight into the development dynamics, the node–place model is utilized with a focus on the I-287 Corridor located in New York, U.S. The node function describes the transport activity and connectedness of the area to other places of interest, which measures the accessibility of the locations, the type of connections, and the number of directions connected. In addition, population, number of workers in the labor force, and degree of a functional mix are also considered for place values. According to the results, four exits are in balanced areas with stable traffic and customer flow support and strong support from local government departments. This case study contributes to a deeper understanding and evaluation of highway accessibility and provides an exciting assessment tool for sustainable development planning. While node–place models cannot predict development, they can be used to understand development dynamics better.

Modeling and simulation of traffic flow based on memory effect and driver characteristics

In this paper, an improvement of the full velocity difference model is presented, which comprehensively considers driver characteristics which include aggressive and timid behaviors and memory effect. The stability condition of the improved model is obtained by making use of the small perturbation method. The Burgers, Korteweg–de Vries and modified Korteweg–de Vries equations are derived by means of the reductive perturbation method. Finally, through numerical simulation, the traffic flow evolution process of the improved model under two typical conditions is observed. The first is the starting process, and the second is the propagation process of disturbance in traffic flow. The results of numerical simulation and theoretical analysis demonstrate that the multiple driver characteristics considered in the improved model have important influences on traffic flow stability and the ease of traffic jams, and the model can quickly recover to its original state in case of the disturbance. • A new car following model is presented, which considers memory effect and driver characteristics including aggressive and timid behaviors. • The stability conditions of the extended model are obtained with the use of the small perturbation method. • The Burgers, Korteweg–de Vries and modified Korteweg–de Vries equations are derived by means of the reduced perturbation method. • The starting process and the propagation process of disturbance are carried out, the results are much closer to actual traffic.

Improved Car-Following Model for Connected Vehicles Considering Backward-Looking Effect and Motion Information of Multiple Vehicles

To alleviate traffic congestion, an improved car-following model for connected vehicles is proposed in this paper by considering backward-looking effect and motion information of multiple vehicles. The backward-looking effect is common in traffic and motion information of multiple vehicles and is beneficial for alleviating traffic congestion. The linear and nonlinear stability of the proposed model is analyzed, and the stability condition and modified Korteweg-de Vries equations of the proposed model are derived. The theoretical analysis results prove the effectiveness of the proposed model in alleviating traffic congestion and improving the stability of the traffic system. Further, numerical simulation is designed to verify the promoting effect of the introduced parameters on the traffic stability and to test the effect of the improved model on large-scale car-following queues. Finally, the next-generation simulation (NGSIM) data sets are used to calibrate the parameters of the improved model. The results show that the improved model can effectively avoid traffic congestion and enhance the stability of traffic flow. The improved model can be used as active safety technology to prevent collision accidents or as a car-following strategy in driverless algorithms.

Adaptive fuzzy predefined‐time fault‐tolerant control for third‐order heterogeneous vehicular platoon systems

AbstractThis article investigates an adaptive fuzzy predefined‐time fault‐tolerant control problem for third‐order heterogeneous vehicular platoon systems (HVPS) with actuator faults. For the purpose of approximating the unknown nonlinear functions, fuzzy logic systems (FLSs) are used. Because the maximum acceleration changes as a result of actuator faults, a quadratic spacing strategy including fault factor is considered. A predefined‐time fault‐tolerant controller is designed to address the impact of actuator faults. The proposed method makes use of the non‐singular terminal sliding mode control (SMC) approach and predefined time stability criterion. Individual vehicle stability, string stability (SS), and traffic flow stability are all guaranteed in a predefined time. Finally, simulations illustrate the usefulness of the given results.

The impact of the density delay on the traffic evolution process in lattice hydrodynamic model under lane change on two lanes

With the increase of the number of vehicles on the road, the traffic density increases gradually. Consequently, the density variation will have an important impact on the traffic dynamic behaviors. Hence, a new lattice hydrodynamic model is constructed by considering the density delay effect concerning the difference between the delayed density and the current density about the leading site on two lanes. The linear stable condition involving the density delay effect is inferred via theory analysis for two-lane traffic flow, which reveals that the density delay effect broadens the stable region in a two-lane system. Moreover, numerical simulation demonstrates that the density delay effect smooths traffic congestion and improves the stability of traffic flow under lane change on two lanes according to the density waves and the hysteresis loops.

An Improved Cellular Automata Traffic Flow Model Considering Driving Styles

An improved cellular automata model (CA model) considering driving styles is proposed to analyze traffic flow characteristics and study traffic congestion’s dissipation mechanism. The data were taken from a particular case in the Next Generation Simulation (NGSIM) program, which selected US-101 as the survey location from 7:50 a.m.–8:05 a.m. to investigate vehicle trajectory information. Different driving styles and the differences in vehicle parameters (speed, acceleration, deceleration, etc.) were obtained using principal component analysis and the k-means clustering method. The selected model was proposed for improvement based on analyzing the existing CA models and combining them with the actual road conditions. Considerations of driving styles and two operation mechanisms (over-acceleration and speed adaptation) were introduced in the improved model. The result obtained after the traffic simulation shows that the improved CA model is effective, and the mutual transformation of different traffic flow phases can be simulated. In the improved CA model, dissipating traffic congestion effectively and balancing the overall flow of the road are realized to improve the traffic capacity up to around 115% compared to the NaSch model and meet the demand of all kinds of drivers expecting to drive at the safest distance, which provides a theoretical basis for relieving traffic congestion. The various driving styles in terms of safety, comfort, and effectiveness are performed differently in the improved CA model. An aggressive driving style contributes to increasing traffic capacity up to around 181% compared to a calm driving style, while the calm style contributes to maintaining traffic flow stability.

The Effect of Pervasive Computing and Driver’s Memory on Connected and Autonomous Vehicles

Pervasive computing techniques provides application guidance and support for intelligent transportation systems. On the basis of connected autonomous vehicles, a extended model has been proposed. This paper focuses on studying the effects of the electronic throttle and driver’s memory. In order to show the influence of these two factors on the model, the extended model is studied by theoretical analysis and numerical methods. Stability conditions are obtained by adding perturbation to the linear analysis, the TDGL equation and mKdV equation are derived by nonlinear analysis, which shows that the phase change behavior of traffic jams can be described by both of them. The numerical simulation results show the correctness of the analysis results. Numerical results show that the effects of driver’s memory and electronic throttle play an key role in the stability of traffic flow, which is consistent with the theoretical analysis results.

On the driver's stochastic nature in car-following behavior: Modeling and stabilizing based on the V2I environment

<abstract> <p>The driver's stochastic nature is one of the important causes of traffic oscillation. To better describe the impact of the driver's stochastic characteristics on car-following behavior, we propose a stochastic full velocity difference model (SFVDM) considering the stochastic variation of the desired velocity. In order to mitigate traffic oscillation caused by driving stochasticity, we further propose a stable speed guidance model (S-SFVDM) by leveraging vehicle-to-infrastructure communication. Stochastic linear stability conditions are derived to demonstrate the prominent influence of the driver's stochasticity on the stability of traffic flow and the improvement of traffic flow stability by the proposed guidance strategy, respectively. We present numerical tests to demonstrate the effectiveness of the proposed models. The results show that the SFVDM can capture the traffic oscillation caused by the driver's stochastic desired velocity and reproduce the same disturbance growth pattern as in the field experiment. The results also indicate that the S-SFVDM can significantly expand the stable area of traffic flow to decrease the negative impact on traffic flow stability caused by the driver's stochastic nature.</p> </abstract>