The invention discloses a holographic road network traffic signal control system and method based on deep reinforcement learning, and the system comprises a linkage controller, and a traffic indication screen, a holographic collection module and a deep learning module which are connected with the linkage controller. The road vehicles are guided to avoid the congested road sections; the linkage controller is used for sending a traffic indication control signal to the traffic indication screen according to the actual road condition and the predicted road condition information; the holographic acquisition module is used for carrying out real-time holographic acquisition on road condition information and vehicle information; and the deep learning module is used for training according to the effective road condition data and the vehicle information data, acquiring the road network traffic prediction model and correcting the road network traffic prediction model. According to the invention, the problem of road congestion can be effectively solved, and the traffic efficiency is greatly improved.
本发明揭示了一种基于深度强化学习的全息路网交通信号控制系统及方法,该系统包括联动控制器以及与所述联动控制器连接的交通指示屏、全息采集模块以及深度学习模块:交通指示屏,用于指示道路车辆的运动状态信息,并指导道路车辆规避拥堵路段;联动控制器,用于根据道路实际路况以及预测路况信息,向所述交通指示屏发送交通指示控制信号;全息采集模块,用于实时全息采集道路路况信息以及车辆信息;深度学习模块,根据有效的道路路况数据以及车辆信息数据进行训练,获取该路网交通预测模型,并对该路网交通预测模型进行修正。本发明能够有效地解决道路拥堵问题,大大提高了交通通行效率。
Holographic road network traffic signal control system and method based on deep reinforcement learning
一种基于深度强化学习的全息路网交通信号控制系统及方法
2022-08-30
Patent
Elektronische Ressource
Chinesisch
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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