Traffic Control Method on Efficiency of Urban Expressway Accompanied Frequent Aggressive Driving Behavior

Abstract

In order to ensure that traffic capacity is stable, and avoid incident congestion is anabatic, a double-layer ramp-metering model is proposed in this paper to control the traffic flow at each on-ramp, nearby incident congestion. The function of the lower model is to recognize where the incident congestion exists, based on an adaptive neural network, whose inputs are traffic flow, velocity and density. The outputs of the lower model are the section number where the congestion occurs and the ramp number which should be controlled. If there is congestion, the upper model would be activated. The function of the upper model is to calculate the control strategy. The required control ramp number from the lower model would be transmitted to the upper model together with the real-time state of controlled section. The upper model would calculate the ramp-metering ratio for each required ramp by minimizing the main time cost, total average queue length and equity index. In order to describe the traffic state more accurately, a tailored traffic model for urban freeway in China is established in the upper model to get the prediction, which considers the speed-limited value on main roadway, and corrects the calculation method of traffic flow state on origin point and exit ramp. The simulation results show the double model is effective to control the incident congestion induced by aggressive behavior.