Optimal management of traffic light timing is one of the most effective
factors in reducing urban traffic. In most old systems, fixed timing was used
along with human factors to control traffic, which is not very efficient in
terms of time and cost. Nowadays, methods in the field of traffic management
are based on the use of artificial intelligence. In this method, by using
real-time processing of video surveillance camera images along with
reinforcement learning, the optimal timing of traffic lights is determined and
applied according to several parameters. In the research, deep learning methods
were used in vehicle detection using the YOLOv9-C model to estimate the number
and other characteristics of vehicles such as speed. Finally, by modeling
vehicles in an urban environment simulator at OpenAI Gym using multi-factor
reinforcement learning and the DQN Rainbow algorithm, timing is applied to
traffic lights at intersections. Additionally, the use of transfer learning
along with retraining the model on images of Iranian cars has increased the
accuracy of the model. The results of the proposed method show a model that is
reasonably accurate in both parts of analyzing surveillance cameras and finding
the optimal timing, and it has been observed that it has better accuracy than
previous research.

使用人工智能和强化学习方法，通过对监控摄像头图像的实时处理，结合 YOLOv9-C 模型进行车辆检测，以及在 OpenAI Gym 的城市环境模拟器中使用多因素强化学习和 DQN 彩虹算法，成功确定并应用交通信号灯的最佳时机。此外，与伊朗车辆图像进行转移学习和重新训练，使模型的准确性得到提高。研究结果表明，该方法在分析监控摄像头和寻找最佳时机的两个部分都具有较高的准确性，且优于先前研究。

利用强化学习技术智能定时交通灯并实时处理监控摄像头图像的交通控制

Traffic control using intelligent timing of traffic lights with  reinforcement learning technique and real-time processing of surveillance  camera images

Cybersecurity is increasingly threatened by advanced and persistent attacks.
As these attacks are often designed to disable a system (or a critical
resource, e.g., a user account) repeatedly, it is crucial for the defender to
keep updating its security measures to strike a balance between the risk of
being compromised and the cost of security updates. Moreover, these decisions
often need to be made with limited and delayed feedback due to the stealthy
nature of advanced attacks. In addition to targeted attacks, such an optimal
timing policy under incomplete information has broad applications in
cybersecurity. Examples include key rotation, password change, application of
patches, and virtual machine refreshing. However, rigorous studies of optimal
timing are rare. Further, existing solutions typically rely on a pre-defined
attack model that is known to the defender, which is often not the case in
practice. In this work, we make an initial effort towards achieving optimal
timing of security updates in the face of unknown stealthy attacks. We consider
a variant of the influential FlipIt game model with asymmetric feedback and
unknown attack time distribution, which provides a general model to consecutive
security updates. The defender's problem is then modeled as a time associative
bandit problem with dependent arms. We derive upper confidence bound based
learning policies that achieve low regret compared with optimal periodic
defense strategies that can only be derived when attack time distributions are
known.

研究了未知隐蔽攻击的上佳安全更新时间问题，针对这个问题提出了基于上置信区间算法的联合病假问题模型，与最优周期性防御策略相比，我们的策略可以取得较低的遗憾。