基于数据聚类技术的城市轨道交通运营特征日分类研究

Classification of Operational Characteristic Days Based on Data Clustering Technology for Urban Rail Transit

首先引入单向OD(Origin-Destination)概率矩阵,以之作为目标线路群体出行规律的表征参数;其次介绍了数据挖掘中的聚类分析方法,并对广泛使用的系统聚类以及快速聚类技术进行重点探讨;然后以某一轨道交通线路为例,构造若干连续运营日的上行OD概率矩阵样本集,并对其进行聚类分析;最后根据聚类结果,将运营日分为"正常工作日"、"周初工作日"、"周末工作日"、"平常周末"和"节假日"5类,为运营部门合理安排行车计划提供决策支持。

One-way OD(origin-destination)probability matrix is firstly introduced,which is taken as the characterization parameters of the mass travel law.Then,the clustering analysis method in data mining is introduced,the widely used Hierarchical Clustering and K-Means Clustering technologies are explored deeply.Based on a certain transit line,a clustering analysis for the sample set of uplink OD probability matrix(for a number of consecutive operation days)is carried out.Finally,according to the result of clustering,the operation days are classified as"normal working days","first working days","last working days","ordinary weekends"and"holidays",providing a decision-making basis for the operation departments in rain schedule arrangement.