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

Traffic light timing optimization is still an active line of research despite
the wealth of scientific literature on the topic, and the problem remains
unsolved for any non-toy scenario. One of the key issues with traffic light
optimization is the large scale of the input information that is available for
the controlling agent, namely all the traffic data that is continually sampled
by the traffic detectors that cover the urban network. This issue has in the
past forced researchers to focus on agents that work on localized parts of the
traffic network, typically on individual intersections, and to coordinate every
individual agent in a multi-agent setup. In order to overcome the large scale
of the available state information, we propose to rely on the ability of deep
Learning approaches to handle large input spaces, in the form of Deep
Deterministic Policy Gradient (DDPG) algorithm. We performed several
experiments with a range of models, from the very simple one (one intersection)
to the more complex one (a big city section).

本文旨在提出一种使用深度学习 DDPG 算法来优化交通信号灯时序的方法，以应对交通数据信息量大的情况，并在简单和复杂的交通网络测试中获得了良好结果。