Urban Traffic Control is a key problem for most big cities. Current approaches to handle the city traffic rely on controlling traffic lights. The systems in operation range from static control of traffic light phases to adaptive systems based on numeric models and traffic sensors. Recently, some planning-based approaches have also been proposed. These approaches work at a higher level of abstraction, but have been found to work well if complemented by low-level systems. We have identified two main difficulties for the wide use of planning techniques in this domain: generating the control models is a difficult task; and some algorithms scale poorly. In this paper we present Automated Planning for Traffic Control (APTC), a control system based on Automated Planning, that successfully overcomes these two problems. It combines techniques that continuously: learn an accurate planning model; and also divide the city for distributed reasoning in order to scale to large city networks. Experimental results show that APTC outperforms static approaches as well as other planning-based systems. We also show that the combination of both approaches improves compared with using only one of them.

中文翻译：

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城市交通控制在线建模与规划

城市交通控制是大多数大城市的关键问题。当前处理城市交通的方法依赖于控制交通灯。运行中的系统范围从交通灯阶段的静态控制到基于数字模型和交通传感器的自适应系统。最近，还提出了一些基于规划的方法。这些方法在更高的抽象级别上工作，但如果辅以低级系统，则发现它们可以很好地工作。我们已经确定了在该领域广泛使用规划技术的两个主要困难：生成控制模型是一项艰巨的任务；并且一些算法扩展性很差。在本文中，我们介绍了交通控制自动规划 (APTC)，这是一种基于自动规划的控制系统，它成功地克服了这两个问题。它结合了以下技术： 学习准确的规划模型；并划分城市进行分布式推理，以扩展到大型城市网络。实验结果表明，APTC 优于静态方法以及其他基于规划的系统。我们还表明，与仅使用其中一种方法相比，两种方法的组合有所改进。