The invention provides a traffic flow prediction and signal control optimization method based on a space-time diagram convolutional network (STGCN), a long short-term memory network (LSTM) and a near-end strategy optimization (PPO) algorithm, and is suitable for intelligent traffic management in a smart city. According to the method, through combination of deep learning and reinforcement learning, urban traffic flow is accurately predicted, traffic signal lamp timing is optimized, urban traffic congestion is reduced, and road utilization efficiency is improved. The method comprises the following steps: firstly, capturing spatial dependence among different road sections through an STGCN model, and processing time dynamic change of traffic flow by using an LSTM model; and then, optimizing signal lamp timing based on a PPO algorithm, and dynamically adjusting a signal time sequence, thereby reducing the vehicle waiting time to the maximum extent and improving the traffic efficiency of the whole traffic flow. In addition, the system integrates external factors such as weather, holidays and festivals, and the adaptive capacity of the model in a complex traffic environment is enhanced. Experiments show that by using the method of the invention, the accuracy of traffic flow prediction is significantly improved, the average waiting time of vehicles is reduced, the traffic volume is increased, and the efficiency of a traffic system is effectively improved. The system is suitable for a large-scale urban traffic network, has good expansibility and real-time performance, and provides powerful technical support for intelligent urban traffic management.

    本发明提出了一种基于时空图卷积网络(STGCN)、长短期记忆网络(LSTM)与近端策略优化(PPO)算法的交通流量预测与信号控制优化方法,适用于智慧城市中的智能交通管理。该方法通过深度学习和强化学习相结合,准确预测城市交通流量并优化交通信号灯配时,旨在减少城市交通拥堵,提高道路利用效率。首先,本发明通过STGCN模型捕捉不同路段之间的空间依赖性,使用LSTM模型处理交通流量的时间动态变化。然后,基于PPO算法对信号灯配时进行优化,动态调整信号时序,从而最大限度地减少车辆等待时间,提升整体交通流的通行效率。此外,系统还整合了天气、节假日等外部因素,增强了模型在复杂交通环境中的适应能力。实验表明,使用本发明的方法,交通流量预测的准确性显著提高,同时车辆平均等待时间减少,通行量提升,交通系统的效率得以有效改善。该系统适用于大规模城市交通网络,具有良好的扩展性和实时性,为智慧城市交通管理提供了强大的技术支持。


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    Titel :

    Traffic flow prediction and signal optimization method based on deep learning and reinforcement learning


    Weitere Titelangaben:

    一种基于深度学习和强化学习的交通流量预测与信号优化方法


    Beteiligte:
    LIN RONGLIANG (Autor:in) / LIN TUXIANG (Autor:in)

    Erscheinungsdatum :

    31.12.2024


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



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