Cordon control with spatially-varying metering rates: A Reinforcement Learning approach
Cordon control with spatially-varying metering rates: A Reinforcement Learning approach
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DOI：
10.1016/j.trc.2018.12.007
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Wei Ni;M. Cassidy
中科院分区：
文献类型：
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作者：
Wei Ni;M. Cassidy
这项工作探讨了如何使用强化学习来重新安排封锁社区周围的交通信号。一个基于RL的控制器的开发表示交通状态的图形结构的数据和定制相应的神经网络架构来处理这些数据。定制使得控制器能够：（i）基于有向图表示对整个街区范围的交通进行建模;（ii）使用表示来识别实时交通测量中的模式;以及（iii）将这些模式捕获到选择最佳警戒线计量率所需的空间表示。选择过程的输入还包括通过警戒线进入的总流入量。速率在一个单独的过程中进行优化，该过程不是本工作的一部分。我们的RL控制器将单独优化的速率分布在通过警戒线提供交通的信号街道连接上。由此产生的计量速率从一个馈线链路到下一个馈线链路而变化。选择过程可以响应于变化的业务模式以短时间间隔重新发生。一旦在几个警戒线上进行了训练，RL控制器就可以部署在城市其他地方的警戒线上，而无需额外的训练。这种便携性特征通过理想化街道网络上的交通模拟得到了证实。测试还表明，控制器可以减少网络的车辆行驶时间远远超过可以通过空间统一的警戒线计量。VHT的额外减少被发现增长更大时，流量表现出更大的不均匀性在网络上。
The work explores how Reinforcement Learning can be used to re-time traffic signals around cordoned neighborhoods. An RL-based controller is developed by representing traffic states as graph-structured data and customizing corresponding neural network architectures to handle those data. The customizations enable the controller to: (i) model neighborhood-wide traffic based on directed-graph representations; (ii) use the representations to identify patterns in real-time traffic measurements; and (iii) capture those patterns to a spatial representation needed for selecting optimal cordon-metering rates. Input to the selection process also includes a total inflow to be admitted through a cordon. The rate is optimized in a separate process that is not part of the present work. Our RL-controller distributes that separately-optimized rate across the signalized street links that feed traffic through the cordon. The resulting metering rates vary from one feeder link to the next. The selection process can reoccur at short time intervals in response to changing traffic patterns. Once trained on a few cordons, the RL-controller can be deployed on cordons elsewhere in a city without additional training.This portability feature is confirmed via simulations of traffic on an idealized street network. The tests also indicate that the controller can reduce the network’s vehicle hours traveled well beyond what can be achieved via spatially-uniform cordon metering. The extra reductions in VHT are found to grow larger when traffic exhibits greater in-homogeneities over the network.