The invention discloses a subway departure scheduling method and system, and the method comprises the following steps: obtaining passenger riding information, line information and regular train information, and building a subway environment state matrix; obtaining metro reward and punishment data, and establishing a departure decision output by metro reward and punishment for judging a departure effect and generating a new departure decision; predicting a new environment state matrix at the next time point according to the subway environment state matrix and the riding information, and updating a departure decision output by the subway reward and punishment; inputting the subway environment state matrix and the reward and punishment data at each time point into a convolutional neural network for training, outputting a train departure decision after training, and executing subway departure according to the train departure decision. According to the method and the system, a Policy gradient algorithm is adopted to carry out reward and punishment output according to a subway environment state, intelligent optimization of subway scheduling can be realized, and an output result of output subway scheduling can more accord with a humanized design in combination with a convolutional neural network.
本发明公开了一种地铁发车调度方法和系统,所述方法包括如下步骤:获取乘客乘车信息、线路信息和列车班车信息,建立地铁环境状态矩阵;获取地铁奖惩数据,建立地铁奖惩输出的发车决策,用于判断发车效果和生成新的发车决策;根据所述地铁环境状态矩阵和乘车信息预测下一个时间点新的环境状态矩阵,并更新所述地铁奖惩输出的发车决策;将每个时间点的地铁环境状态矩阵和奖惩数据输入到卷积神经网络进行训练,输出训练后发车决策,根据所述发车决策执行地铁发车。所述方法和系统采用Policy gradient算法根据地铁环境状态进行奖惩输出的方式,可以实现地铁调度的智能优化,结合卷积神经网络可以使得输出地铁调度的输出结果更加符合人性化设计。
Subway departure scheduling method and system
一种地铁发车调度方法和系统
2021-08-20
Patent
Electronic Resource
Chinese
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