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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(More)
In this work, we apply single neuron 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(More)
Rough sets theory is a new tool for processing fuzzy and uncertain knowledge, and has already been applied to many areas successfully. In this paper, a freeway traffic flow model based on rough sets and Elman neural network is put forward. The main idea of this approach is that some redundant features of sample data are reduced by rough sets firstly, then(More)
Freeway congestion problem can be addressed employing many different measures. Ramp metering is the most widely used control measure, and is an efficient way to control and upgrade freeway traffic by regulating the number of vehicles entering the freeway. This paper proposes an iterative learning approach for the freeway density control under ramp metering(More)
In this work, we apply the iterative learning method to address the traffic density control problem in a macroscopic level freeway environment with ramp metering. The macroscopic model to describe the evolution of freeway traffic flow is firstly established. Then traffic density is selected as the control variable in place of traffic occupancy, and the(More)
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