城市轨道交通客流与站点特征分析  

Analysis of Passenger Flow and Station Characteristics in Urban Rail Transit

作　　者：李敏祥 谢佳茵[1] 金辉 李永浈 LI Minxiang;XIE Jiayin;JINHui;LI Yongzhen(School of Automobile and Traffic Engineering,Liaoning University of Technology,Jinzhou,Liaoning,121001,China;School of Logistics,Liaoning Vocational University of Technology,Jinzhou,Liaoning,121001,China)

机构地区：[1]辽宁工业大学汽车与交通工程学院,辽宁锦州121001 [2]辽宁理工职业大学物流学院,辽宁锦州121001

出　　处：《智能城市应用》2024年第7期26-28,共3页Smart City Application

摘　　要：交通客流与站点特征的精准分析,对缓解城市交通拥堵,促进轨道交通的良好运营起着至关重要的作用。文中以成都市轨道交通为例,基于AFC数据完成客流特征分析,并采用改进的k-means算法对成都地铁1、2、3号线104个站点进行聚类,最终分为居住导向型、就业导向型、职住交错型、职住交错偏居住型、职住交错偏就业型和综合型6类站点。望为后续车站发展规划及周边土地利用等提供参考依据。The precise analysis of passenger flow and station characteristics plays a crucial role in alleviating urban traffic congestion and promoting the good operation of rail transit.Taking Chengdu rail transit as an example,this article completes passenger flow characteristic analysis based on AFC data,and uses an improved k-means algorithm to cluster 104 stations on Chengdu Metro Lines 1,2 and 3.Finally,it is divided into six types of stations:residential oriented,employment oriented,occupational residential staggered,occupational residential staggered,occupational residential staggered,and employment oriented,and comprehensive,which will provide reference for the development planning of subsequent stations and the utilization of surrounding land.

关 键 词：轨道交通 AFC数据 客流特征 K-MEANS算法 

分 类 号：U491[交通运输工程—交通运输规划与管理]