scholarly journals A Modified Control Method for Congested Traffic in Car-following Model

2013 ◽  
Author(s):  
Yu Cui ◽  
Hong-xia Ge
Keyword(s):  
Control Method ◽  
Car Following ◽  
Congested Traffic ◽  
Car Following Model

A modified control method for congested traffic in car-following model

2014 ◽  
Vol 3 (4) ◽  
pp. 457-462 ◽  
Author(s):  
Jie Bao ◽  
Yu Cui ◽  
Hong-di He ◽  
Peng-jun Zheng ◽  
Hong-xia Ge
Keyword(s):  
Control Method ◽  
Car Following ◽  
Congested Traffic ◽  
Car Following Model

A control method for congested traffic in the coupled map car-following model

2009 ◽  
Vol 18 (10) ◽  
pp. 4208-4216 ◽  
Author(s):  
Shen Fei-Ying ◽  
Ge Hong-Xia ◽  
Zhang Hui ◽  
Yu Han-Mei ◽  
Lei Li
Keyword(s):  
Control Method ◽  
Car Following ◽  
Congested Traffic ◽  
Car Following Model

A Control Method for Congested Traffic in the Car-Following Model

2012 ◽  
Vol 29 (5) ◽  
pp. 050502 ◽  
Author(s):  
Hong-Xia Ge ◽  
Jian Yu ◽  
Siu-Ming Lo
Keyword(s):  
Control Method ◽  
Car Following ◽  
Congested Traffic ◽  
Car Following Model

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.

A new control method integrated into the coupled map car-following model for suppressing traffic jams

2016 ◽  
Vol 88 (1) ◽  
pp. 663-671 ◽  
Author(s):  
Linjiang Zheng ◽  
Tong Zhou ◽  
Weining Liu ◽  
Dong Chen ◽  
Dihua Sun
Keyword(s):  
Control Method ◽  
Car Following ◽  
Traffic Jams ◽  
Car Following Model

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.

Full velocity difference car-following model considering desired inter-vehicle distance

2018 ◽  
Vol 29 (02) ◽  
pp. 1850018
Author(s):  
Tong Xin ◽  
Liu Yi ◽  
Cheng Rongjun ◽  
Ge Hongxia
Keyword(s):  
Theoretical Analysis ◽  
Numerical Simulations ◽  
Traffic Congestion ◽  
Control Method ◽  
Stability Conditions ◽  
Velocity Difference ◽  
Car Following ◽  
Car Following Model ◽  
The Stability

Based on the full velocity difference car-following model, an improved car-following model is put forward by considering the driver’s desired inter-vehicle distance. The stability conditions are obtained by applying the control method. The results of theoretical analysis are used to demonstrate the advantages of our model. Numerical simulations are used to show that traffic congestion can be improved as the desired inter-vehicle distance is considered in the full velocity difference car-following model.

scholarly journals A Support Vector Regression Approach for Investigating Multianticipative Driving Behavior

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Bin Lu ◽  
Shaoquan Ni ◽  
Scott S. Washburn
Keyword(s):  
Support Vector Regression ◽  
Driving Behavior ◽  
Support Vector ◽  
Trajectory Data ◽  
Car Following ◽  
Congested Traffic ◽  
Car Following Model ◽  
Importance Analysis ◽  
Model Training ◽  
Vehicle Trajectory

This paper presents a Support Vector Regression (SVR) approach that can be applied to predict the multianticipative driving behavior using vehicle trajectory data. Building upon the SVR approach, a multianticipative car-following model is developed and enhanced in learning speed and predication accuracy. The model training and validation are conducted by using the field trajectory data extracted from the Next Generation Simulation (NGSIM) project. During the model training and validation tests, the estimation results show that the SVR model performs as well as IDM model with respect to the model prediction accuracy. In addition, this paper performs a relative importance analysis to quantify the multianticipation in terms of the different stimuli to which drivers react in platoon car following. The analysis results confirm that drivers respond to the behavior of not only the immediate leading vehicle in front but also the second, third, and even fourth leading vehicles. Specifically, in congested traffic conditions, drivers are observed to be more sensitive to the relative speed than to the gap. These findings provide insight into multianticipative driving behavior and illustrate the necessity of taking into account multianticipative car-following model in microscopic traffic simulation.

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