Single Neuron Based Freeway Traffic Density Control via Ramp Metering

2010 ◽  
Author(s):  
Xinrong Liang ◽  
Jianye Li ◽  
Nongzhen Luo
Keyword(s):  
Single Neuron ◽  
Ramp Metering ◽  
Traffic Density ◽  
Freeway Traffic ◽  
Density Control

An Iterative Learning Approach for Freeway Traffic Density Control

2011 ◽  
Vol 317-319 ◽  
pp. 1394-1397 ◽  
Author(s):  
Xin Rong Liang ◽  
Xiao Yan Wu ◽  
Jian Ye Li
Keyword(s):  
Traffic Flow ◽  
Flow Model ◽  
Control Variable ◽  
Iterative Learning ◽  
Ramp Metering ◽  
Traffic Density ◽  
Nonlinear Feedback ◽  
Freeway Traffic ◽  
Traffic Flow Model ◽  
Density Control

In this work, we apply iterative learning method to address the traffic density control problem in a macroscopic level freeway environment with ramp metering. The second-order traffic flow model is firstly formulated. Then traffic density is selected as the control variable in place of traffic occupancy. Based on the traffic flow model and in conjunction with nonlinear feedback theory, an iterative learning based traffic density controller is designed. Finally, the iterative learning based feedback controller is simulated in Matlab software. Simulation results show that this method has good dynamic and steady-state performance, and can achieve an almost perfect tracking performance.

Freeway Ramp Metering Based on Cell Transmission Model and Single Neuron

2013 ◽  
Vol 423-426 ◽  
pp. 2877-2881
Author(s):  
Yan Di Ye ◽  
Yan Yan Liu ◽  
Xin Rong Liang ◽  
Chao Jun Dong
Keyword(s):  
Traffic Congestion ◽  
Single Neuron ◽  
Learning Algorithm ◽  
Ramp Metering ◽  
Transmission Model ◽  
Cell Transmission Model ◽  
Freeway Traffic ◽  
Tracking Process ◽  
Cell Transmission ◽  
Control Objective

In this work, we apply single neuron method to relieve freeway traffic congestion. We consider a freeway composed of cells and entry/exit ramps, and formulate the ramp metering problem as a density tracking process. The cell transmission model (CTM) is firstly formulated and ramp control objective is determined. Based on CTM and single neuron, a freeway ramp metering system is then designed, and the learning algorithm of single neuron is given in detail. Finally, the ramp metering system is simulated in MATLAB software. The results show that this method can effectively deal with this class of control problem, and can achieve a perfect density tracking performance. This ramp metering can eliminate traffic congestion and maintain traffic flow stability.

Fuzzy Logic Based Traffic Density Control via Ramp Metering

2011 ◽  
Vol 97-98 ◽  
pp. 888-891
Author(s):  
Xin Rong Liang ◽  
Di Qian Wang
Keyword(s):  
Fuzzy Logic ◽  
Traffic Flow ◽  
Flow Model ◽  
Tracking Error ◽  
Control Variable ◽  
Ramp Metering ◽  
Traffic Density ◽  
Rule Base ◽  
Traffic Flow Model ◽  
Density Control

In this work, we apply fuzzy logic method to address the traffic density control problem in a macroscopic level freeway environment with ramp metering. The second-order traffic flow model is firstly formulated. Then traffic density is selected as the control variable in place of traffic occupancy. Based on the traffic flow model and in conjunction with nonlinear feedback theory, a fuzzy logic based traffic density controller is designed. The ramp metering rate is determined by the fuzzy control according to density tracking error and error variation. Triangle curves are used for the membership functions of the fuzzy variables. The rule base including 56 fuzzy rules is also established. Finally, the fuzzy logic based feedback controller is simulated in Matlab software. Simulation results show that this method has good dynamic and steady-state performance, and can achieve an almost perfect tracking performance.

Asymptotic Tracking Control of Freeway Traffic Density via Ramp Metering

2012 ◽  
Vol 253-255 ◽  
pp. 1686-1690
Author(s):  
Xiao Hong Fan ◽  
Yong Feng Ju
Keyword(s):  
Tracking Control ◽  
Flow System ◽  
Ramp Metering ◽  
Traffic Density ◽  
Unknown Parameters ◽  
External Disturbances ◽  
Freeway Traffic ◽  
Control Gain ◽  
Asymptotic Tracking ◽  
Ramp Signal

The method of the robust integral of the sign of the error was used to design the ramp signal for traffic flow system of freeway with uncertaines . Uncertain terms are allowed to be modelled or unmodelled.The unmodelled terms in the model were estimated by there bounded differential . Compared with other ramp metering method, this controller is designed without approximately linearizing the complicated nonlinear model and requiring the upper bound of unmodelled terms known .The control gain is reduced .The method is verified to be robust subject to both external disturbances and unmodelled dynamics ,and to be adaptive to unknown parameters . The numerical example shows good tracking performance .

Freeway Traffic Density Control Based on CMAC

2012 ◽  
Vol 220-223 ◽  
pp. 2726-2729
Author(s):  
Xin Rong Liang ◽  
Guo Yong Man ◽  
Jian Ye Li
Keyword(s):  
Control Method ◽  
Learning Algorithm ◽  
Fast Response ◽  
Traffic Density ◽  
Cerebellar Model Articulation Controller ◽  
Flow Process ◽  
Freeway Traffic ◽  
Composition Control ◽  
Density Control ◽  
Traffic Systems

Aiming at the nonlinear and time-variable characteristics of freeway traffic systems, a composition control method based on CMAC (Cerebellar Model Articulation Controller) and PID controller is applied to address the traffic density control problem in a macroscopic level freeway environment with ramp metering. Firstly, the second-order traffic flow model to describe freeway flow process is established. Then the principle of CMAC-PID composition control is formulated, and the learning algorithm is given in detail. Based on the model and in conjunction with feedback control theory, CMAC-PID based traffic density controller is designed. Finally, the controller is simulated in MATLAB software. The results show that this controller designed has fast response, good dynamic and steady-state performance. It can achieve a desired traffic density along the freeway mainline.

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