scholarly journals A New Car-Following Model considering Driving Characteristics and Preceding Vehicle’s Acceleration

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Yong Zhang ◽  
Ping Ni ◽  
Minwei Li ◽  
Hao Liu ◽  
Baocai Yin
Keyword(s):  
Traffic Flow ◽  
Clustering Algorithm ◽  
Traffic Data ◽  
Velocity Difference ◽  
Car Following ◽  
The Past ◽  
Preceding Vehicle ◽  
Car Following Model ◽  
The Stability ◽  
Anticipation Effects

In the past decades, many improved car-following models based on the full velocity difference (FVD) model have been developed. But these models do not consider the acceleration of leading vehicle. Some of them consider individual anticipation behavior of drivers, but they either do not quantitatively determine the types of driving or artificially divide the driving types rather than deriving them from actual traffic data. In this paper, driver’s driving styles are firstly categorized based on actual traffic data via data mining and clustering algorithm. Secondly, a new car-following model based on FVD model is developed, taking into account individual anticipation effects and the acceleration of leading vehicle. The effect of driving characteristics and leading vehicle’s acceleration on car-following behavior is further analyzed via numerical simulation. The results show that considering the acceleration of preceding vehicle in the model improves the stability of traffic flow and different driving characteristics have different influence on the stability of traffic flow.

A Control Method for Car Following Model Considering Changing Behaviors

2012 ◽  
Vol 198-199 ◽  
pp. 954-957
Author(s):  
Xiang Pei Meng ◽  
Rong Jun Cheng ◽  
Hong Xia Ge
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Control Method ◽  
Velocity Difference ◽  
Lane Changing ◽  
Traffic System ◽  
Car Following ◽  
Congested Traffic ◽  
Car Following Model ◽  
The Stability

We propose a simple control method to suppress two-lane traffic congestion for full velocity difference (for short, FVD) car-following model. The influence of lane changing behaviors is also studied in the stability of two-lane traffic flow under the boundary condition, and the friction interference which is from the neighbor lane has been taken into account. We derive the stability conditions by the control method. The feedback signals, which include vehicular information from both lanes, acting on the two-lane traffic system have been extended to the FVD car-following model. Theoretically, lane changing behaviors can break the stability of two-lane traffic flow and aggravate traffic perturbation, but it is proven that the congested traffic in two-lane traffic flow could be suppressed by using this control method.

Nonlinear analysis of a new car-following model accounting for the global average optimal velocity difference

2016 ◽  
Vol 30 (27) ◽  
pp. 1650327 ◽  
Author(s):  
Guanghan Peng ◽  
Weizhen Lu ◽  
Hongdi He
Keyword(s):  
Nonlinear Analysis ◽  
Traffic Flow ◽  
Stable Region ◽  
Velocity Difference ◽  
Optimal Velocity ◽  
Car Following ◽  
Global Average ◽  
Car Following Model ◽  
The Stability ◽  
Difference Effect

In this paper, a new car-following model is proposed by considering the global average optimal velocity difference effect on the basis of the full velocity difference (FVD) model. We investigate the influence of the global average optimal velocity difference on the stability of traffic flow by making use of linear stability analysis. It indicates that the stable region will be enlarged by taking the global average optimal velocity difference effect into account. Subsequently, the mKdV equation near the critical point and its kink–antikink soliton solution, which can describe the traffic jam transition, is derived from nonlinear analysis. Furthermore, numerical simulations confirm that the effect of the global average optimal velocity difference can efficiently improve the stability of traffic flow, which show that our new consideration should be taken into account to suppress the traffic congestion for car-following theory.

The effects of drivers’ aggressive characteristics on traffic stability from a new car-following model

2016 ◽  
Vol 30 (18) ◽  
pp. 1650243 ◽  
Author(s):  
Guanghan Peng ◽  
Li Qing
Keyword(s):  
Stability Analysis ◽  
Nonlinear Analysis ◽  
Traffic Flow ◽  
Linear Stability Analysis ◽  
Stable Condition ◽  
Mkdv Equation ◽  
Stable Region ◽  
Car Following ◽  
Car Following Model ◽  
The Stability

In this paper, a new car-following model is proposed by considering the drivers’ aggressive characteristics. The stable condition and the modified Korteweg-de Vries (mKdV) equation are obtained by the linear stability analysis and nonlinear analysis, which show that the drivers’ aggressive characteristics can improve the stability of traffic flow. Furthermore, the numerical results show that the drivers’ aggressive characteristics increase the stable region of traffic flow and can reproduce the evolution and propagation of small perturbation.

An improved car-following model considering the impact of safety messages

2018 ◽  
Vol 32 (32) ◽  
pp. 1850398 ◽  
Author(s):  
Tenglong Li ◽  
Fei Hui ◽  
Xiangmo Zhao
Keyword(s):  
Traffic Congestion ◽  
Dominant Factor ◽  
Velocity Difference ◽  
Car Following ◽  
Safety Message ◽  
Warning Messages ◽  
Gray Correlation ◽  
Car Following Model ◽  
The Stability ◽  
The Impact

The existing car-following models of connected vehicles commonly lack experimental data as evidence. In this paper, a Gray correlation analysis is conducted to explore the change in driving behavior with safety messages. The data mining analysis shows that the dominant factor of car-following behavior is headway with no safety message, whereas the velocity difference between the leading and following vehicle becomes the dominant factor when warning messages are received. According to this result, an extended car-following model considering the impact of safety messages (IOSM) is proposed based on the full velocity difference (FVD) model. The stability criterion of this new model is then obtained through a linear stability analysis. Finally, numerical simulations are performed to verify the theoretical analysis results. Both analytical and simulation results show that traffic congestion can be suppressed by safety messages. However, the IOSM model is slightly less stable than the FVD model if the average headway in traffic flow is approximately 14–20 m.

An Extended Car-Following Model in a Multi-Interaction Driving System

2012 ◽  
Vol 178-181 ◽  
pp. 2717-2720
Author(s):  
Man Xian Tuo
Keyword(s):  
Numerical Simulation ◽  
Traffic Flow ◽  
Flow Model ◽  
Extended Model ◽  
Traffic Flow Model ◽  
Driving System ◽  
Car Following ◽  
Traffic Jams ◽  
Car Following Model ◽  
The Stability

An extended traffic flow model is proposed by introducing the multiple information of preceding cars. The linear stability condition of the extended model is obtained, which shows that the stability of traffic flow is improved by considering the interaction of preceding cars to the following car. Numerical simulation shows that the traffic jams are suppressed efficiently by taking into account the multiple information of the preceding cars.

Analysis of drivers' characteristics in car-following theory

2014 ◽  
Vol 28 (24) ◽  
pp. 1450191 ◽  
Author(s):  
Geng Zhang ◽  
Di-Hua Sun ◽  
Hui Liu ◽  
Min Zhao
Keyword(s):  
Numerical Simulation ◽  
Traffic Flow ◽  
Mkdv Equation ◽  
Reductive Perturbation Method ◽  
Linear Stability Theory ◽  
Traffic Density ◽  
Car Following ◽  
Car Following Model ◽  
The Stability ◽  
The Impact

In recent years, the influence of drivers' behaviors on traffic flow has attracted considerable attention according to Transportation Cyber Physical Systems. In this paper, an extended car-following model is presented by considering drivers' timid or aggressive characteristics. The impact of drivers' timid or aggressive characteristics on the stability of traffic flow has been analyzed through linear stability theory and nonlinear reductive perturbation method. Numerical simulation shows that the propagating behavior of traffic density waves near the critical point can be described by the kink–antikink soliton of the mKdV equation. The good agreement between the numerical simulation and the analytical results shows that drivers' characteristics play an important role in traffic jamming transition.

scholarly journals Driver’s Anticipation and Memory Driving Car-Following Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Ammar Jafaripournimchahi ◽  
Lu Sun ◽  
Wusheng Hu
Keyword(s):  
Traffic Flow ◽  
Nonlinear Stability ◽  
Relative Velocity ◽  
Mkdv Equation ◽  
Signalized Intersection ◽  
Stability Conditions ◽  
Car Following ◽  
Car Following Model ◽  
Linear And Nonlinear ◽  
The Stability

We developed a new car-following model to investigate the effects of driver anticipation and driver memory on traffic flow. The changes of headway, relative velocity, and driver memory to the vehicle in front are introduced as factors of driver’s anticipation behavior. Linear and nonlinear stability analyses are both applied to study the linear and nonlinear stability conditions of the new model. Through nonlinear analysis a modified Korteweg-de Vries (mKdV) equation was constructed to describe traffic flow near the traffic near the critical point. Numerical simulation shows that the stability of traffic flow can be effectively enhanced by the effect of driver anticipation and memory. The starting and breaking process of vehicles passing through the signalized intersection considering anticipation and driver memory are presented. All results demonstrate that the AMD model exhibit a greater stability as compared to existing car-following models.

scholarly journals An Improved Car-Following Model Accounting for Impact of Strong Wind

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Dawei Liu ◽  
Zhongke Shi ◽  
Wenhuan Ai
Keyword(s):  
Fuel Consumption ◽  
Traffic Flow ◽  
Influence Factor ◽  
Driving Safety ◽  
Wind Condition ◽  
Strong Wind ◽  
Wind Force ◽  
Car Following ◽  
Car Following Model ◽  
The Stability

In order to investigate the effect of strong wind on dynamic characteristic of traffic flow, an improved car-following model based on the full velocity difference model is developed in this paper. Wind force is introduced as the influence factor of car-following behavior. Among three components of wind force, lift force and side force are taken into account. The linear stability analysis is carried out and the stability condition of the newly developed model is derived. Numerical analysis is made to explore the effect of strong wind on spatial-time evolution of a small perturbation. The results show that the strong wind can significantly affect the stability of traffic flow. Driving safety in strong wind is also studied by comparing the lateral force under different wind speeds with the side friction of vehicles. Finally, the fuel consumption of vehicle in strong wind condition is explored and the results show that the fuel consumption decreased with the increase of wind speed.

scholarly journals A Modified Car-following Model Considering Traffic Density and Acceleration of Leading Vehicle

2020 ◽  
Vol 10 (4) ◽  
pp. 1268
Author(s):  
Xudong Cao ◽  
Jianjun Wang ◽  
Chenchen Chen
Keyword(s):  
Traffic Flow ◽  
Linear Stability Theory ◽  
Traffic Density ◽  
Neutral Stability ◽  
Difference Model ◽  
Velocity Difference ◽  
Car Following ◽  
Car Following Model ◽  
The Difference ◽  
Improved Model

Although the difference between the velocity of two successive vehicles is considered in the full velocity difference model (FVDM), more status information from preceding vehicles affecting the behavior of car-following has not been effectively utilized. For improving the performance of the FVDM, an extended modified car-following model taking into account traffic density and the acceleration of a leading vehicle (DAVD, density and acceleration velocity difference model) is presented under the condition of vehicle-to-vehicle (V2V) communications. Stability in the developed model is derived through applying linear stability theory. The curves of neutral stability for the improved model indicate that when the driver pays more attention to the traffic status in front, the traffic flow stability region is larger. Numerical simulation illustrates that traffic flow disturbance could be suppressed by gaining more information on preceding vehicles.

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