Automated Traffic State Optimization in the Weaving Area of Urban Expressways by a Reinforcement Learning-Based Cooperative Method of Channelization and Ramp Metering

The traffic congestion problem on urban expressways, especially in the weaving areas, has become severe. Some cooperative methods have been proven to be more effective than a separate approach optimizing state areas expressways. However, method that combines channelization with ramp metering not presented and its effectiveness examined yet. Thus, fill this research gap, study proposes reinforcement learning-based of achieve automated optimization area. This uses an unmanned aerial vehicle collect real flow data, four control strategies (i.e., two kinds methods, method, metering) baseline (without controls) are designed simulation platform (Simulation Urban Mobility). speed distributions different each lane were obtained analyzed study. results show is superior other significantly higher increases speeds. can increase average speeds lane-1, lane-2, lane-3 by 14.51%, 14.81%, 37.03%, respectively. Findings contribute improvement efficiency safety area