在轨道交通运营管理过程中,不同车站间客流的时间分布特性及规律直接决定了客流组织方案。明晰车站间客流分型及特征,对合理配置客流组织方案大有脾益。相对于单车站客流时间类型,车站间客流类型的影响因素多样且复杂,为此,本文首先从时间、空间、结构三个角度对车站间客流分类特征进行分析,并通过谱聚类方法压缩分类的搜索空间,从而达到更加精准的类型划分。利用轮廓系数与戴维森堡丁指数对比不同方法的分类结果,证明所提出的谱聚类方法相对于k-means等其他方法具有更好的分类效果。以苏州地铁2020年数据为例,通过提出的方法寻找出7种车站间客流分型,该结果可应用于预测模型训练等领域。

In the process of rail transit operation management, the characteristics and rules of time distribution of passenger flow between different stations directly determine the passenger flow organization scheme. The clarification of inter-station passenger flow time distribution types is beneficial to the reasonable configuration of passenger flow organization scheme. In this paper, we firstly analyze the classification characteristics of inter-station passenger flow from three perspectives of time, space and structure, and compress the search space of classification by spectral clustering method to achieve more accurate type classification. The classification results of different methods are compared using Silhouette Coefficient and Davies-Bouldin Index, and it is demonstrated that the proposed spectral clustering method has better classification results compared with other methods such as k-means. Taking Suzhou Metro 2020 data as an example, seven types of inter-station passenger flow time distribution are found by the proposed method, and the results can be applied to areas such as prediction model training.

苏州市科技发展计划（SS201830）