MAGT-toll:一种多代理强化学习方法,用于动态的交通拥堵定价
Jiaming Lu1, Chuanyang Hong2, Rui Wang3
1School of Business Administration, Southwestern University of Finance and Economics, Chengdu, China.
PloS one
|November 18, 2024
概括
本研究介绍了一种使用多代理增强学习和变压器架构的新动态交通拥堵定价模型. 这种方法有效地减少了城市交通拥堵和旅行时间.
科学领域:
- 智能运输系统 智能运输系统
- 城市规划中的人工智能
- 交通工程是交通工程.
背景情况:
- 城市拥堵是一个重大挑战,传统的静态收费系统未能适应动态的交通需求.
- 由于计算需求和网络协调要求,实施动态拥堵定价是复杂的.
研究的目的:
- 提出一种新的动态交通拥堵定价模型.
- 解决动态定价中的计算复杂性和协调挑战.
- 改善交通流动,减少城市环境中的拥堵.
主要方法:
- 利用多代理强化学习与变压器架构进行动态定价.
- 使用编码器解码器结构将问题转换为序列建模任务.
- 嵌入的代理结构和位置编码灵感来自图形变压器.
- 开发了一个微模拟环境,用于城市道路上的离散收费率定价方案.
主要成果:
- 在各种交通需求场景中,在拥堵指标方面取得了实质性的改进.
- 实现了总体旅行时间的显著减少.
- 在模拟的城市道路网络中有效缓解交通拥堵.
结论:
- 拟议的多代理增强学习模型与变压器架构对于动态交通拥堵定价是有效的.
- 该模型成功地解决了计算复杂性和网络协调问题.
- 这种方法为缓解城市交通拥堵和提高旅行效率提供了有希望的解决方案.
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