摘要
研究一种可有效促进城市轨道交通行车组织管理效能的模糊神经网络算法。利用超限学习机模块对站点客流量进行初步卷积,使用卷积神经网络将考察线路内的各站点客流量数据进行汇总,同时构建其他线路的参照数据,使用二值化模块形成放行信号灯建议数据。应用该系统后,客流峰值车辆满载率显著下降,客流估值车辆满载率显著提升,客流估值发车间隙显著增加但并未影响到旅客的站内滞留时间。表明该算法可以有效提升城市轨道交通的运行效率和经济效益。
This paper studies a fuzzy neural network algorithm which can effectively promote the efficiency of urban rail transit operation organization and management. The overrun learning machine module is used to convolute the passenger flow of each station, the convolution neural network is used to summarize the passenger flow data of each station in the inspection line, and the reference data of other lines is constructed, and the binary module is used to form the release signal light recommendation data. After the application of the system, the peak vehicle load rate of passenger flow is significantly reduced, the estimated vehicle load rate of passenger flow is significantly increased, and the departure gap of passenger flow is significantly increased, but it does not affect the residence time of passengers in the station. It is considered that the algorithm can effectively improve the operation efficiency and economic benefits of urban rail transit.
作者
王静
Wang Jing(Xi’an Railway Vocational and Technical College,Xi’an 710014,China)
出处
《电子测量技术》
北大核心
2021年第5期118-122,共5页
Electronic Measurement Technology
