Freeway ramp control based on single neuron

In an effort to relieve traffic congestion on freeways, various ramp metering algorithms have been employed to regulate the inputs to freeways from entry ramps. In this paper, we consider a freeway system composed of freeway sections and their entry/exit ramps, and formulate the ramp control problem as a density tracking process. Firstly, the macroscopic model to describe the evolution of freeway traffic flow is established and the objective of ramp control is determined. Based on the traffic flow model and in conjunction with nonlinear feedback theory, a freeway ramp control system based on single neuron is designed. According to density errors and error increments, single neuron control is used to determine the ramp metering rate in order to make the actual traffic density approach the desired one. Finally, the ramp control system is simulated in MATLAB software. The results show that the control system has very small density tracking errors. This system can eliminate traffic congestion and maintain traffic flow stability.