Delay Margin Analysis of a Large-Scale Optimal Velocity Model using the Parallel Processing Delay Margin Finder (parDMF)

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.

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Bifurcation control of optimal velocity model through anticipated effect and response time-delay feedback methods

Physica A Statistical Mechanics and its Applications ◽

10.1016/j.physa.2021.125972 ◽

2021 ◽

pp. 125972

Author(s):

Xueyi Guan ◽

Rongjun Cheng ◽

Hongxia Ge

Keyword(s):

Time Delay ◽

Response Time ◽

Velocity Model ◽

Bifurcation Control ◽

Optimal Velocity ◽

Optimal Velocity Model

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OPTIMAL VELOCITY MODEL WITH RELATIVE VELOCITY

International Journal of Modern Physics C ◽

10.1142/s0129183106009084 ◽

2006 ◽

Vol 17 (01) ◽

pp. 65-73 ◽

Cited By ~ 5

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.

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

Discrete Dynamics in Nature and Society ◽

10.1155/2018/5326947 ◽

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.

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