Abstract
ABSTRACTIn order to alleviate the congestion in the mainline of urban expressway caused by the uncertainty of the traffic flow, a linked-bottleneck control method for urban expressway on-ramp is proposed in this paper. Firstly, the downstream dynamic critical occupancy of the on-ramp under uncertainty is predicted by using ESN (Echo State Network). Then, a function for dividing the master-slave ramps is designed to dynamically determine the ramp numbers required coordination and expand the range of coordination control. Considering the designed function changes over time and some factors such as the traffic accidents, weather and so on also affect it, the BP neural network is used to improve its accuracy. The proposed method calculates the on-ramp regulating rate based on the downstream dynamic critical occupancy and expand the range of coordination control by the designed function, which can adapt the changes of actual traffic flow and overcome the defects of some methods such as Bottleneck, Linked-control, Swarm and so on. The simulations show that, compared with some existing methods, the congestion duration and the high density duration in the mainline of the urban expressway are significantly reduced by using the proposed method in this paper.
Highlights
With the increase of vehicles driving in the urban expressway, the uncertainty of the traffic flow increases, which leads to the increasing congestion of the vehicle in the mainline of urban expressway
In order to alleviate the congestion in the mainline of urban expressway caused by the uncertainty of the traffic flow, a linked-bottleneck control method for urban expressway on-ramp is proposed in this paper
The proposed method calculates the on-ramp regulating rate based on the downstream dynamic critical occupancy and expand the range of coordination control by the designed function, which can adapt the changes of actual traffic flow and overcome the defects of some methods such as Bottleneck, Linked-control, Swarm and so on
Summary
With the increase of vehicles driving in the urban expressway, the uncertainty of the traffic flow increases, which leads to the increasing congestion of the vehicle in the mainline of urban expressway. According to the scope of the control, the existing strategies can be classified as local ramp regulating control and coordinated ramp metering control The former adjusts the traffic flow of the expressway on-ramps by using sensor measurements near the single ramp, which includes feedforward control methods such as the demand-capacity strategy (Papageorgiou, Hadj-Salem, & Blosseville, 1991) and model free adaptive control (Hou & Yan, 2009); feedback control methods such as the ALINEA strategies (Iordanidou, Papamichail, Roncoli, & Papageorgiou, 2014; Wang, Papageorgiou, Gaffney, Papamichail, & Guo, 2010, September), neural network and fuzzy-logic based methods (Vukanovic & Ernhofer, 2006). The simulations show that, compared with some existing methods, the congestion duration and the high density duration in the mainline of the urban expressway are significantly reduced by using the proposed method in this paper
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