Expressway traffic efficiency improving method based on reinforced learning variable speed-limit control

Abstract

The invention discloses an expressway traffic efficiency improving method based on reinforced learning variable speed-limit control. The method comprises the steps that variable speed-limit values are determined in real time according to a reinforced learning method, an intelligent body perceives the running states of traffic flows on an expressway according to traffic flow data, a speed-limit value action is selected for the current state, a return valve of state transition caused by the action is calculated, the intelligent body traverses all state-action combinations till all state-action return values are convergent, and the intelligent body acquires the optimal speed-limit value action in the different traffic flow states off line; the intelligent body automatically selects the optimal speed-limit value corresponding to the current state and issues the optimal speed-limit value according to the real-time traffic flow data, and the controlled traffic flow data and speed-limit values are transmitted to a control center, so that the intelligent body continuously learns. According to the method, the defect that in the past, the subjective arbitrariness is generated when the corresponding relation between the traffic flow state and the speed-limit values in variable speed-limit control is determined is overcome, and the anti-jamming capability of a control system is improved; the affecting law of the speed-limit values on traffic efficiency improving is continuously mined through the intelligent body, therefore, feedback regulation is conducted on the variable speed-limit values according to the real-time traffic flow data, and the road traffic efficiency of a bottleneck road section in variable speed-limit control is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of intelligent transportation and traffic control, and in particular relates to a method for improving the traffic efficiency of expressways based on reinforcement learning variable speed limit control. Background technique [0002] Variable speed limit control, as a traffic control strategy that is more and more widely used to improve the efficiency of expressway traffic, its control effect is closely related to the method used in the process of determining the variable speed limit value. As a closed-loop structure, reinforcement learning enables the agent to continuously learn the optimal speed limit values ​​corresponding to different traffic flow states through the feedback adjustment of the control effect to the control strategy, effectively improving the effect of variable speed limit control and variable speed limit control. Control the rationality of the speed limit value. Therefore, the variable sp...

AI Technical Summary

Problems solved by technology

[0003] The determination of the corresponding speed limit value under different traffic flow states in the existing variable speed limit control strategy depends on the subjective determination of the engineer's experience. achieve optimal control

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