Research on Signal Control Strategy Based on MA2C Method Optimization and Sumo Simulation Real-time Inference
Currently,traffic management departments rely heavily on manual experience to solve urban traffic congestion problems.However,this method lacks mathematical theoretical support and practical verification,and cannot provide optimal solutions from a global and holistic perspective.This article is based on Sumo traffic simulation and uses the MA2C multi-agent reinforcement learning model to develop targeted signal timing reinforcement learning rules.After multiple rounds of training,the signal timing with the highest corresponding traffic transportation efficiency is obtained.At the same time,Sumo simulation modeling and data processing processes were coded and modularized to improve operational efficiency,ultimately achieving full automation of traffic signal timing optimization and processes.
万方数据
维普
