基于时段客流特征聚类的地铁运营时段划分  

Division of Metro Operation Periods Based on Feature Clustering of Passenger Flow

作　　者：陈东洋 陈德旺 江世雄 徐宁 CHEN Dong-Yang;CHEN De-Wang;JIANG Shi-Xiong;XU Ning(College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350108,China;Key Laboratory of Intelligent Metro of Universities in Fujian Province,Fuzhou University,Fuzhou 350108,China)

机构地区：[1]福州大学数学与计算机科学学院,福州350108 [2]福州大学智慧地铁福建省高校重点实验室,福州350108

出　　处：《计算机系统应用》2021年第3期256-261,共6页Computer Systems & Applications

基　　金：国家自然科学基金面上项目(61976055);智慧地铁福建省高校重点实验室建设项目(53001703,50013203)。

摘　　要：准确合理的运营时段划分方案是制定地铁列车开行方案的前提和基础,也是提高地铁运营效率的重要方式.为了合理划分地铁运营时段,本文构建时段客流特征向量以划分地铁运营时段.以10 min为时间间隔对全日运营时段进行分段,并根据时段内的客流变化特点构建各时段的特征向量.并以此为基础采用K-means算法进行聚类,同时以肘部法则、轮廓系数等聚类评估指标对结果进行评价,以确定最优聚类数,进而得到最优的运营时段划分方案.最后以福州地铁一号线为例,给出了该路线的运营时段划分方案,验证了该方法的可行性.An accurate and reasonable division scheme of operation periods is the premise and foundation for formulating metro train operation plans, and it is also an important way to improve metro operation efficiency. The feature vectors of passenger flow are constructed to divide metro operation periods. At an interval of 10 min, the operation period in a day is segmented, and the feature vectors of all the periods are constructed according to the characteristics of passenger flow changes in their corresponding periods. The K-means algorithm is used for clustering, and the results are evaluated by cluster evaluation indicators such as elbow method and silhouette coefficient to determine the optimal number of clusters,obtaining the optimal division scheme of operational periods. Finally, the division scheme of operation periods of Line 1 of Fuzhou Metro is given as an example, which verifies the feasibility of this method.

关 键 词：地铁 客流 运营时段划分 聚类 K-MEANS算法 

分 类 号：U293.13[交通运输工程—交通运输规划与管理]