Improving the resilience of urban road networks suffering from various disruptions has been a central focus for urban emergence management. However, to date the effective methods which may mitigate the negative impacts caused by the disruptions, such as road accidents and natural disasters, on urban road networks is highly insufficient. This study proposes a novel adaptive signal control strategy based on a doubly dynamic learning framework, which consists of deep reinforcement learning and day-to-day traffic dynamic learning, to improve the network performance by adjusting red/green time split. In this study, red time split is regarded as extra traffic flow to discourage drivers to use affected roads, so as to reduce congestion and improve the resilience when urban road networks are subject to different levels of disruptions. In addition, we utilize the convolution neural network as Q-network to approximate Q values, link flow distribution and link capacity are regarded as the state space, and actions are denoted as red/green time split. A small network is utilized as a numerical example, and a fixed time signal control and other two adaptive signal controls are employed for the comparisons with the proposed one. The results show that the proposed adaptive signal control based on deep reinforcement learning can achieve better resilience in most of the cases, particularly in the scenarios of moderate and severe disruptions. This study may shed light on the advantages of the proposed adaptive signal control dealing with major emergencies compared to others.

中文翻译：

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基于自适应信号控制的城市道路网络弹性分析：深度强化学习的日常交通动态

改善遭受各种破坏的城市道路网络的弹性一直是城市应急管理的重点。但是，迄今为止，有效的方法可能不足以减轻诸如交通事故和自然灾害之类的干扰对城市道路网络造成的负面影响。这项研究提出了一种基于双动态学习框架的新型自适应信号控制策略，该策略包括深度强化学习和日常交通动态学习，以通过调整红色/绿色时间间隔来改善网络性能。在这项研究中，红色时间分割被视为额外的交通流量，以阻止驾驶员使用受影响的道路，从而在城市道路网络受到不同程度的干扰时减少拥堵并提高弹性。此外，Q值，链接流分布和链接容量被视为状态空间，而动作被表示为红色/绿色时间划分。将一个小型网络用作数值示例，并使用固定时间信号控制和其他两个自适应信号控制与所提出的进行比较。结果表明，提出的基于深度强化学习的自适应信号控制在大多数情况下，尤其是在中度和严重中断情况下，可以实现更好的弹性。与其他相比，这项研究可能会揭示所提出的自适应信号控制在处理重大紧急情况方面的优势。