CO2 emission control in new CM car-following model with feedback control of the optimal estimation of velocity difference under V2X environment

2021 ◽  
Author(s):  
Peng Guang-Han ◽  
Tang Rui ◽  
Kuang Hua ◽  
Tan Hui-Li ◽  
Chen Tao
Keyword(s):  
Feedback Control ◽  
Co2 Emission ◽  
Emission Control ◽  
Optimal Estimation ◽  
Velocity Difference ◽  
Car Following ◽  
Car Following Model

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.

Feedback control scheme of traffic jams based on the coupled map car-following model

2013 ◽  
Vol 22 (9) ◽  
pp. 090205 ◽  
Author(s):  
Tong Zhou ◽  
Di-Hua Sun ◽  
Min Zhao ◽  
Hua-Min Li
Keyword(s):  
Feedback Control ◽  
Car Following ◽  
Traffic Jams ◽  
Control Scheme ◽  
Car Following Model

Non-lane-based full velocity difference car following model

2010 ◽  
Vol 389 (21) ◽  
pp. 4654-4662 ◽  
Author(s):  
Sheng Jin ◽  
Dianhai Wang ◽  
Pengfei Tao ◽  
Pingfan Li
Keyword(s):  
Velocity Difference ◽  
Car Following ◽  
Car Following Model ◽  
Lane Based

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.

Car-following model based delay feedback control method with the gyroidal road

2019 ◽  
Vol 30 (09) ◽  
pp. 1950073 ◽  
Author(s):  
Cong Zhai ◽  
Weitiao Wu
Keyword(s):  
Feedback Control ◽  
Traffic Flow ◽  
Delay Time ◽  
Control Method ◽  
Gain Coefficient ◽  
Car Following ◽  
Controller Gain ◽  
Car Following Model ◽  
Feedback Control Method ◽  
Delay Feedback Control

Connected vehicles are expected to become commercially available by the next decade. In this work, we propose a delay feedback control method for car-following model on a gyroidal road. By using the Hurwitz criteria and the condition for transfer function in terms of [Formula: see text]-norm, the impact of controller gain coefficient and the delay time on the performance of traffic flow is investigated. Based on the bode curve, we verify that the designed delay feedback controller is effective in suppressing traffic congestion and reducing energy consumption. The enhanced traffic flow model is more sensitive to the controller gain coefficient and delay time at downhill situation compared to the uphill situation. The conclusion obtained from the simulation example is consistent with the theoretical analysis.

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.

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