Recently, finding fundamental properties for traffic state representation is more critical than complex algorithms for traffic signal control (TSC).In this paper, we (1) present a novel, flexible and straightforward method advanced max pressure (Advanced-MP), taking both running and queueing vehicles into consideration to decide whether to change current phase; (2) novelty design the traffic movement representation with the efficient pressure and effective running vehicles from Advanced-MP, namely advanced traffic state (ATS); (3) develop an RL-based algorithm template Advanced-XLight, by combining ATS with current RL approaches and generate two RL algorithms, "Advanced-MPLight" and "Advanced-CoLight". Comprehensive experiments on multiple real-world datasets show that: (1) the Advanced-MP outperforms baseline methods, which is efficient and reliable for deployment; (2) Advanced-MPLight and Advanced-CoLight could achieve new state-of-the-art. Our code is released on Github.
翻译:最近,寻找交通州代表的基本特性比交通信号控制(TSC)的复杂算法更为关键。 在本文中,我们(1)提出一种新的、灵活和直截了当的先进最大压力(高级-MP)方法,将运行车辆和排队车辆都考虑在内,以决定是否改变当前阶段;(2)以高级-MP(即高级交通州)高效压力和有效运行车辆(即高级交通州)高效压力和有效运行车辆来设计交通州代表;(3)制定基于RL的高级XLight算法模板,将苯丙胺类兴奋剂与目前的RL方法相结合,并产生两种RL算法,即“高级-MPLight”和“高级-Coight”。关于多个现实世界数据集的综合实验表明:(1)高级MP超出基线方法,这是高效和可靠的部署方法;(2)高级-MPLight和高级-COLight可以实现新的状态。我们的代码在Github上发布。