Impact of interruption probability of the current optimal velocity on traffic stability for car-following model

2021 ◽  
Author(s):  
Xiaoqin Li ◽  
Yanyan Zhou ◽  
Guanghan Peng
Keyword(s):  
Stable Condition ◽  
Velocity Model ◽  
Mkdv Equation ◽  
The Self ◽  
Optimal Velocity ◽  
Traffic Jam ◽  
Traffic System ◽  
Car Following ◽  
Optimal Velocity Model ◽  
Car Following Model

Traffic interruption is one of the important factors resulting in traffic jam. Therefore, a new optimal velocity model is established involving the traffic interruption probability for self-expected velocity. Linear stable condition and mKdV equation are deduced with regard to the self-interruption probability of the current optimal velocity from linear stable analysis and nonlinear analysis, respectively. Moreover, numerical simulation reveals that the traffic self-interruption probability of the current optimal velocity can increase traffic stability, which certifies that the traffic self-interruption probability of the current optimal velocity plays important influences on traffic system.

scholarly journals A New Car-Following Model with Consideration of Dynamic Safety Distance

2018 ◽  
Vol 2018 ◽  
pp. 1-5
Author(s):  
Tao Wang ◽  
Jing Zhang ◽  
Guangyao Li ◽  
Keyu Xu ◽  
Shubin Li
Keyword(s):  
Traffic Flow ◽  
Velocity Model ◽  
Safe Distance ◽  
Optimal Velocity ◽  
New Model ◽  
Car Following ◽  
Optimal Velocity Model ◽  
Safety Distance ◽  
Car Following Model ◽  
The Impact

In the traditional optimal velocity model, safe distance is usually a constant, which, however, is not representative of actual traffic conditions. This paper attempts to study the impact of dynamic safety distance on vehicular stream through a car-following model. Firstly, a new car-following model is proposed, in which the traditional safety distance is replaced by a dynamic term. Then, the phase diagram in the headway, speed, and sensitivity spaces is given to illustrate the impact of a variable safe distance on traffic flow. Finally, numerical methods are conducted to examine the performance of the proposed model with regard to two aspects: compared with the optimal velocity model, the new model can suppress traffic congestion effectively and, for different safety distances, the dynamic safety distance can improve the stability of vehicular stream. Simulation results suggest that the new model is able to enhance traffic flow stability.

scholarly journals An Extended Car-Following Model at Signalised Intersections

2018 ◽  
Vol 2018 ◽  
pp. 1-26 ◽  
Author(s):  
Hongxing Zhao ◽  
Ruichun He ◽  
Changxi Ma
Keyword(s):  
Velocity Model ◽  
Measured Data ◽  
Velocity Difference ◽  
Optimal Velocity ◽  
Car Following ◽  
Optimal Velocity Model ◽  
Trajectory Simulation ◽  
Car Following Model ◽  
Point Data ◽  
Isolated Point

An extended car-following model is proposed on the basis of experimental analysis to improve the performance of the traditional car-following model and simulate a microscopic car-following behaviour at signalised intersections. The new car-following model considers vehicle gather and dissipation. Firstly, the parameters of optimal velocity, generalised force and full velocity difference models are calibrated by measured data, and the problems and causes of the three models are analysed with a realistic trajectory simulation as an evaluation criterion. Secondly, an extended car-following model based on the full optimal velocity model is proposed by considering the vehicle gather and dissipation. The parameters of the new car-following model are calibrated by the measured data, and the model is compared with comparative models on the basis of isolated point data and the entire car-following process. Simulation results show that the optimal velocity, generalised force, and full velocity difference models cannot effectively simulate a microscopic car-following behaviour at signalised intersections, whereas the new car-following model can avoid a collision and has a high fit degree for simulating the measured data of the car-following behaviour at signalised intersections.

STABILITY AND KINK–ANTIKINK SOLITON SOLUTION FOR TOTAL GENERALIZED OPTIMAL VELOCITY MODEL

2008 ◽  
Vol 19 (09) ◽  
pp. 1321-1335 ◽  
Author(s):  
WEN-XING ZHU ◽  
LEI JIA
Keyword(s):  
Soliton Solution ◽  
Kdv Equation ◽  
Velocity Model ◽  
Linear Analysis ◽  
Analysis Method ◽  
Optimal Velocity ◽  
Car Following ◽  
Optimal Velocity Model ◽  
Car Following Model ◽  
Differential Difference Equation

We proposed a new car-following model named as total generalized optimal velocity model (TGOVM) based on the analysis of the previous models. TGOVM is superior to the previous models in stabilizing the uniform traffic flow by considering all the front influencing factors: headways, relative velocities, and interactions. The linear analysis result showed its superiority to the GOVM, FLOVM, and FLRVM. The nonlinear analysis method is adopted to analyze this model, which described by a differential-difference equation. The modified Korteweg-de Vries (KdV) equation is derived and the kink-antikink soliton solution is obtained near the critical point. The simulation results show that the stabilization is enhanced by the improvement.

OPTIMAL VELOCITY MODEL WITH RELATIVE VELOCITY

2006 ◽  
Vol 17 (01) ◽  
pp. 65-73 ◽  
Author(s):  
SHIRO SAWADA
Keyword(s):  
Nonlinear Analysis ◽  
Relative Velocity ◽  
Velocity Model ◽  
Fundamental Diagram ◽  
Optimal Velocity ◽  
Traffic Jam ◽  
Optimal Velocity Model ◽  
Linear And Nonlinear ◽  
The Stability ◽  
Good Agreement

The optimal velocity model which depends not only on the headway but also on the relative velocity is analyzed in detail. We investigate the effect of considering the relative velocity based on the linear and nonlinear analysis of the model. The linear stability analysis shows that the improvement in the stability of the traffic flow is obtained by taking into account the relative velocity. From the nonlinear analysis, the relative velocity dependence of the propagating kink solution for traffic jam is obtained. The relation between the headway and the velocity and the fundamental diagram are examined by numerical simulation. We find that the results by the linear and nonlinear analysis of the model are in good agreement with the numerical results.

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.

A REVISED STOCHASTIC OPTIMAL VELOCITY MODEL CONSIDERING THE VELOCITY GAP WITH A PRECEDING VEHICLE

2011 ◽  
Vol 22 (09) ◽  
pp. 1005-1014 ◽  
Author(s):  
KEIZO SHIGAKI ◽  
JUN TANIMOTO ◽  
AYA HAGISHIMA
Keyword(s):  
Velocity Model ◽  
Model Parameters ◽  
Fundamental Diagram ◽  
Cellular Automata Model ◽  
Optimal Velocity ◽  
Car Following ◽  
Proposed Model ◽  
Preceding Vehicle ◽  
Long Time ◽  
Car Following Model

The stochastic optimal velocity (SOV) model, which is a cellular automata model, has been widely used because of its good reproducibility of the fundamental diagram, despite its simplicity. However, it has a drawback: in SOV, a vehicle that is temporarily stopped takes a long time to restart. This study proposes a revised SOV model that suppresses this particular defect; the basic concept of this model is derived from the car-following model, which considers the velocity gap between a particular vehicle and the preceding vehicle. A series of simulations identifies the model parameters and clarifies that the proposed model can reproduce the three traffic phases: free, jam, and even synchronized phases, which cannot be achieved by the conventional SOV model.

Studying Car-Following Dynamics on the Basis of the HighD Dataset

2020 ◽  
Vol 2674 (8) ◽  
pp. 813-822
Author(s):  
Valentina Kurtc
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Velocity Model ◽  
Driver Model ◽  
Good Precision ◽  
Optimal Velocity ◽  
Car Following ◽  
Optimal Velocity Model ◽  
Position Time Series ◽  
Vehicle Trajectory

A large-scale naturalistic vehicle trajectory dataset from German highways, highD, was used to investigate the car-following behavior of individual drivers. These data include trajectories of 110,000 vehicles recorded for a duration of 16.5 h. Solving a nonlinear optimization problem, the intelligent driver model and the optimal velocity model with two leaders in interaction were calibrated by minimizing the deviations between the observed and simulated gaps when following the prescribed leading vehicle. The obtained calibration errors ranged between 5.2% and 6.9%, which were slightly lower than previous findings. This was explained by the shorter highD trajectories, predominantly free-flow traffic, and the good precision metrics of this dataset. The optimal velocity model with multivehicle anticipation resulted in lower calibration errors. This confirmed that natural drivers take into account several leading vehicles ahead. The ratio between interdriver and intradriver variability was investigated by performing global and platoon calibrations. Intradriver variation accounted for a larger portion of the calibration errors than interdriver variation. We analyzed the acceleration time-series of the natural highD and artificial drivers using simulations of two car-following models. A new cumulative measure, proportional to the energy of the follower’s position time-series curve, was calculated both for natural and modeled drivers. Human drivers had higher energy and demonstrated more acceleration fluctuations, sometimes behaving irrationally. In contrast, artificial drivers followed the logical rules incorporated in the model, resulting in a smoother acceleration profile. This led to less fuel consumption and gas emissions.

An extended car-following model considering random safety distance with different probabilities

2018 ◽  
Vol 32 (05) ◽  
pp. 1850056 ◽  
Author(s):  
Jufeng Wang ◽  
Fengxin Sun ◽  
Rongjun Cheng ◽  
Hongxia Ge ◽  
Qi Wei
Keyword(s):  
Stable Condition ◽  
Maximum Velocity ◽  
Linear Stability Theory ◽  
Single Type ◽  
Velocity Function ◽  
Optimal Velocity ◽  
Car Following ◽  
Safety Distance ◽  
Car Following Model ◽  
Optimal Velocity Function

Because of the difference in vehicle type or driving skill, the driving strategy is not exactly the same. The driving speeds of the different vehicles may be different for the same headway. Since the optimal velocity function is just determined by the safety distance besides the maximum velocity and headway, an extended car-following model accounting for random safety distance with different probabilities is proposed in this paper. The linear stable condition for this extended traffic model is obtained by using linear stability theory. Numerical simulations are carried out to explore the complex phenomenon resulting from multiple safety distance in the optimal velocity function. The cases of multiple types of safety distances selected with different probabilities are presented. Numerical results show that the traffic flow with multiple safety distances with different probabilities will be more unstable than that with single type of safety distance, and will result in more stop-and-go phenomena.

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