Traffic elasticity regulation and control method and system based on reinforcement learning

Abstract

The invention relates to a traffic elasticity regulation and control method and system based on reinforcement learning. The method comprises the following steps: dividing a traffic network into a plurality of areas, and collecting traffic flow information of each area; determining a macroscopic basic diagram of each area according to the traffic flow information; based on the macroscopic fundamental diagram, determining the flow level of each region during supply and demand balance; determining the imbalance rate of the traffic network according to the flow level; determining a critical imbalance threshold value of the traffic network through seepage analysis according to the imbalance rate; establishing a multi-agent reinforcement learning model, and performing learning training on the multi-agent reinforcement learning model according to the traffic flow information, the flow level and the critical imbalance threshold to generate a trained multi-agent reinforcement learning model; and regulating and controlling the actual traffic network by using the trained multi-agent reinforcement learning model to make the current unbalance rate of the actual traffic network smaller than the critical unbalance threshold. The optimal regulation and control effect can be achieved in the shortest regulation and control period.

Description

technical field [0001] The invention relates to the interdisciplinary field of traffic elasticity, traffic control and network science, in particular to a traffic elasticity control method and system based on reinforcement learning. Background technique [0002] In recent years, catastrophic emergencies still occur from time to time, and these events often cause huge social and economic losses. For example, the Suez Canal ship blockage incident that occurred in March 2021 was caused by a cargo ship encountering a cross wind while passing through the Suez Canal and the hull was swayed, directly blocking the canal channel. According to relevant estimates, due to the impact of this incident, the Suez Canal may lose about US$400 million for every day of blockage. In view of the possible disastrous consequences of emergencies, how to improve the emergency response capabilities of the urban system is a pain point for urban managers. In this context, Holling proposed the concept ...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a traffic elastic control method and system based on reinforcement learning to solve the problem of long control cycle and poor control effect

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