面向拥堵传播阻断的城轨站点分类

Classification of urban rail stations based on passenger flow congestion propagation

针对拥堵客流的传播阻断问题,研究了一种城轨站点分类方法.首先,通过分析影响站点拥堵传播阻断能力的相关因素,选取了站点客流量、站点运输能力和拓扑位置指标构建指标体系;其次,采用SOM神经网络和模糊分类算法对多指标数据进行分类,获取了不同时段下具有不同拥堵阻断能力的站点类别,实现面向拥堵传播阻断的站点分类;最后,采用北京市轨道交通实际运营数据对模型进行验证.研究结果表明:该方法能够在城轨系统不同时段下,随着客流量和运输能力的变化,准确识别不同类型的站点,并为后续研究城轨网络的大客流拥堵传播模型提供站点参数分析依据.

Aiming at the problem of blocking the passenger flow congestion propagation,a classification method of urban rail stations is studied.First,through the analysis of relevant factors affecting the blocking of the passenger flow congestion propagation,an index system is constructed by selecting passenger flow,transportation capacity and topological location of the station.Then,SOM neural network and fuzzy classification algorithm are used to classify the multi-index data,and the station categories with different congestion blocking capacity in different periods are obtained,realizing the station classification for congestion propagation blocking.Finally,the actual operation data of Beijing rail transit is used to verify the model.The results show that this method can accurately identify different types of stations in different periods of urban rail transit system,with changing passenger flow and transportation capacity;this provides reference for the subsequent study of congestion propagation model under large passenger flow.