摘要
随着中国社会经济和城镇化的迅速发展,城市居民的职住空间关系不断发生变化。在城市功能结构失衡导致交通拥堵问题日趋严重的背景下,居民轨道交通出行需求也在不断增加。以北京市为例,基于轨道交通刷卡数据,从网络客运量、客流时空分布状态等方面深入挖掘乘客出行特征及规律。运用k均值聚类算法(K-Means聚类),以不均衡系数为指标对北京市各地铁站乘客出行时空分布进行聚类分析,研究居民出行规律与城市功能结构间的不均衡关系,为城市轨道交通运营组织策略的研究提供理论支撑。
With the rapid development of China’s economy and urbanization,the spatial relationship of urban residents is constantly changing.In the context of the increasing traffic congestion caused by an imbalance of an urban functional structure,the demand for resident travel is also increasing.With Beijing taken as an example,based on the automatic fare collection(AFC)card data,the network passenger volume,passenger flow space-time distribution,and other aspects of passenger travel characteristics are analyzed.Using k-means clustering,the subway stations of Beijing are clustered with unbalanced coefficients to study the unbalanced relationship between regular travel patterns and functional urban structures,whereby theoretical support for the study of an urban rail transit operation organization strategy is provided.
作者
曹敬浛
许琰
孙立山
赵昇辉
王艳
CAO Jinghan;XU Yan;SUN Lishan;ZHAO Shenghui;WANG Yan(College of Metropolitan Transportation,Beijing University of Technology,Beijing 100124)
出处
《都市快轨交通》
北大核心
2021年第2期71-78,85,共9页
Urban Rapid Rail Transit
基金
北京市科委项目(Z191100002519002)
北京市教委项目(KM202010005001)
关键词
城市轨道交通
刷卡数据
客流特征
站点功能
urban rail transit
AFC data
passenger flow characteristics
station features
