热带地理2017，Vol.37Issue(1)：102-111,10.DOI:10.13284/j.cnki.rddl.002918
基于地铁客流的广州地铁站点类型识别
Clustering of Metro Stations in Guangzhou based on Passenger Flow
摘要
Abstract
Big data such as transportation card data, cell phone data provide data support for studies on trip characteristics analysis and city spatial structure. These studies require efficient methods to deal with the large volume, high dimension and information redundancy problems with big data. With the increase of data dimension, the complexity of data increases dramatically, making it impossible for human to understand the data and extract features based on expert knowledge. In this paper, principal component analysis (PCA) was used for dimensionality reduction of and feature extraction from passenger flow data of Guangzhou metro stations. The PCA process calculates the scoring coefficients for each component automatically without prior knowledge and eliminates the disturbance of subjective factors. Six principal components, extracted out of 36 variables, in this research kept 91.41% information of the original data. The first two components represented the passenger flow characteristics of residence-oriented stations and employment-oriented stations, respectively. K-means clustering of metro stations was performed based on the features extracted and 7 types of metro stations were recognized:residence-oriented, employment-oriented, spatially mismatched, mixed but more of residence-oriented, mixed but more of employment-oriented, comprehensive and traffic hub and entertainment stations. Among the 137 stations of Guangzhou Metro, over 85% were commuting-related, 46 stations were residence-oriented, 14 were employment-oriented, and 59 were of mixed nature of the two, with more of one or another. Further research on the spatial distribution of different clusters of the metro stations revealed a ring structure of Guangzhou City. The diversity and types of metro stations varied as the distance to city center increased, reflecting the spatial distribution of urban functions. Distribution of multi-types of metro stations such as employment-oriented, comprehensive and mixed but more of employment-oriented ones in the central city area such as Yuexiu District and Liwan District implied the high degree of development and the diversity of urban functions of these areas. Employment-oriented and comprehensive stations were intensely distributed in Tianhe District, making it the core administrative area and business area and the new city center of Guangzhou City. The main types of metro stations in Haizhu District were spatially mismatched stations and mixed but more of residence-oriented stations. In suburban areas such as Baiyun District and Panyu District, the main type of metro station was spatially mismatched stations, suggesting the decrease in the diversity of urban function. And only residence-oriented stations were found in the periphery areas of the city. Far away from the center of Guangzhou City, Nansha District and Foshan City developed into regional centers with some spatially mismatched stations distributed. The results of this study demonstrate that metro passenger flow data can not only reflect the spatial and temporal patterns of residents’ travel behavior, but also provide new data and a new perspective for urban spatial structure research.关键词
地铁站点/地铁客流/K-均值聚类算法/主成分分析/广州Key words
metro stations/metro passenger flow/clustering/principal component analysis/Guangzhou Metro分类
交通工程引用本文复制引用
谭章智,李少英,黎夏,刘小平,陈逸敏..基于地铁客流的广州地铁站点类型识别[J].热带地理,2017,37(1):102-111,10.基金项目
国家自然科学基金项目（41401432）；广东省教育厅青年创新人才项目（2014KQNCX107）；广东省自然科学基金博士启动项目 （）
