基于MFD-DDPG的道路交通区域的周边控制方法
Guorong Zheng1, Yuke Liu1, Yazhou Fu1
1Beijing Key Lab of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing 100144, China.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
本研究介绍了区域交通控制的Q-learning和DDPG算法,优化了没有交通确定性的周边控制. 拟议的模型,MFD-QL和MFD-DDPG,展示了快速融合和优越的交通管理效应.
科学领域:
- 交通工程是交通工程.
- 城市规划 城市规划
- 人工智能的人工智能
背景情况:
- 城市扩张导致严重的交通拥堵,阻碍经济增长和社会活动.
- 有效的区域交通管理和控制对于城市治理至关重要.
- 根据区域拥堵特征优化交通控制是一个复杂的研究挑战.
研究的目的:
- 为城市地区开发先进的交通控制模型.
- 解决区域交通优化问题,而不依赖于实时交通确定.
- 应用强化学习算法用于周边控制策略.
主要方法:
- 专注于用于交通分析的宏观基本图 (MFD).
- 从强化学习中引入Q学习 (QL) 算法.
- 从深度强化学习中实现深度决定性政策梯度 (DDPG) 算法.
- 开发MFD-QL和MFD-DDPG周边控制模型.
主要成果:
- 进行了数值分析和模拟实验.
- MFD-QL和MFD-DDPG算法显示了快速趋同到稳定的状态.
- 在优化区域外围控制方面,实现了更高的控制效果.
结论:
- 拟议的MFD-QL和MFD-DDPG模型对区域外围控制有效.
- 强化学习方法为复杂的交通管理挑战提供了有希望的解决方案.
- 这些算法有助于改善城市交通流量和减少拥堵.
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