文章提出了一种基于机器学习和控制算法的智慧城市交通信号灯优化方法.通过使用长短期记忆(Long Short Term Memory,LSTM)网络进行交通流量预测,捕捉了交通流的时空关系,为信号灯控制提供了准确的输入数据.采用比例积分微分(Proportion Integration Differentiation,PID)控制器,通过实时调整信号灯来实现对交通流的精准控制.仿真实验结果表明,该方法在不同交通场景下均表现出色,有效提高了交叉口的通行效率,减少了拥堵现象,为城市交通的智能化管理提供了可行的解决方案.
Research on Intelligent City Traffic Signal Optimization Based on Machine Learning
This paper proposes a smart city traffic light optimization method based on machine learning and control algorithms.First,by using Long Short Term Memory(LSTM)network for traffic flow prediction,the spatiotemporal relationship of traffic flow is captured,providing accurate input data for signal light control.Secondly,a Proportional Integral Derivative(PID)controller is used to achieve precise control of traffic flow by adjusting the signal lights in real time.Simulation experiment results show that this method performs well in different traffic scenarios,effectively improves intersection efficiency,reduces congestion,and provides a feasible solution for intelligent management of urban traffic.
machine learningLong Short Term Memory(LSTM)smart citytraffic light
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