ES-CTC：高速公路交通协同智能控制的深度神经进化模型
ES-CTC: A Deep Neuroevolution Model for Cooperative Intelligent Freeway Traffic Control
摘要：
高速公路协同智能交通控制是智能交通系统的一个重要应用，有望提高高速公路网络的机动性。在本文中，我们提出了一个深度神经进化模型，称为ES-CTC，以实现协同控制方案的匝道调节，差异可变限速和车道变换控制代理，以改善高速公路交通。在该模型中，利用图卷积网络从交通传感器中学习更有意义的空间模式，设计了一个知识共享层用于不同智能体之间的通信。所提出的神经网络结构允许不同的代理人共享知识和异步执行动作。为了解决延迟奖励和行动不确定性问题，采用进化策略对随机交通需求下的智能体进行训练。在高速公路模拟路段上的实验结果表明，ES-CTC是一种可行的方法，并优于现有的几种方法
英文摘要：
Cooperative intelligent freeway traffic control is an important application in intelligent transportation systems, which is expected to improve the mobility of freeway networks. In this paper, we propose a deep neuroevolution model, called ES-CTC, to achieve a cooperative control scheme of ramp metering, differential variable speed limits and lane change control agents for improving freeway traffic. In this model, the graph convolutional networks are used to learn more meaningful spatial pattern from traffic sensors, a knowledge sharing layer is designed for communication between different agents. The proposed neural networks structure allows different agents share knowledge with each other and execute action asynchronously. In order to address the delayed reward and action asynchronism issues, the evolutionary strategy is utilized to train the agents under stochastic traffic demands. The experimental results on a simulated freeway section indicate that ES-CTC is a viable approach and outperforms several existing methods
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
