Traffic signal control (TSC) is a high-stakes domain that is growing in importance as traffic volume grows globally. An increasing number of works are applying reinforcement learning (RL) to TSC; RL can draw on an abundance of traffic data to improve signalling efficiency. However, RL-based signal controllers have never been deployed. In this work, we provide the first review of challenges that must be addressed before RL can be deployed for TSC. We focus on four challenges involving (1) uncertainty in detection, (2) reliability of communications, (3) compliance and interpretability, and (4) heterogeneous road users. We show that the literature on RL-based TSC has made some progress towards addressing each challenge. However, more work should take a systems thinking approach that considers the impacts of other pipeline components on RL.
翻译:随着全球交通量的增长,交通信号控制(TSC)是一个重要性越来越大的高风险领域,越来越多的作品正在对TSC应用强化学习(RL);RL可以利用大量的交通数据来提高信号效率,然而,基于RL的信号控制器从未部署过。在这项工作中,我们首先审查在为TSC部署RL之前必须应对的挑战。我们侧重于四个挑战:(1) 检测的不确定性,(2) 通信的可靠性,(3) 合规和可解释性,(4) 道路使用者各异。我们表明,基于RL的TSC的文献在应对每一项挑战方面都取得了一定进展。然而,更多的工作应该采取一种系统思考方法,考虑其他管道部件对RL的影响。