城市交通流量预测和信号控制优化多模式和多代理强化学习框架
Ruokun Wang1, Ju Zhang2, Xikui Wang2
1School of Communication and Signal, Nanjing Vocation Institute of Railway Technology, Nanjing, 210031, China. 2014560010@njrts.edu.cn.
Scientific reports
|February 6, 2026
概括
本研究介绍了MM-STMAP,这是一个新的城市交通管理框架. 它使用深度强化学习和环境数据来减少智能城市的交通拥堵和排放.
科学领域:
- 城市规划和智慧城市发展
- 运输系统中的人工智能
- 环境科学与可持续发展
背景情况:
- 快速的城市化加剧了交通拥堵,导致温室气体排放增加和空气质量差.
- 现有的交通管理系统往往忽视了城市环境的复杂生态和动态性质.
- 需要适应性,综合性解决方案,以实现可持续的城市流动.
研究的目的:
- 引入MM-STMAP,一个城市交通管理框架.
- 将多模式感知与深度强化学习相结合,以加强交通控制.
- 通过纳入环境数据来解决传统交通系统的生态限制.
主要方法:
- 利用时空图形卷积网络来建模复杂的交通模式.
- 集成的实时环境数据,包括气象因素.
- 采用线性注意力机制来提高计算效率,并采用多智能增强学习结构来协调交通信号.
主要成果:
- 在经验评估中,MM-STMAP与现有的交通管理方法相比,表现优越.
- 实现了交通流效率的显著提高,并减少了车辆的延误.
- 成功地整合了来自交通传感器和环境报告的异质数据流.
结论:
- MM-STMAP为城市流动提供了一种全面而适应性的方法.
- 该框架支持通过优化交通流量和尽量减少排放来开发可持续的智能城市基础设施.
- 将环境数据整合到交通管理中对于城市规划中的生态考虑至关重要.
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