基于AFC数据挖掘的轨道交通站点分类研究
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摘要
轨道交通站点的分类对于研究不同类别站点的客流规律、周边土地利用情况以及发展趋势都有着重要影响。基于AFC数据,综合多种有效性指标确定分类数,采用主成分分析、k-means聚类、多元线性回归等方法,定性分析与定量分析相结合对站点进行类别划分。将苏州轨道交通1、2号线共58个站点分为4类,为站点分类别后续研究以及轨道交通发展研究奠定基础。
The classification of rail transit stations has an important impact on the study of passenger flow pat terns, surrounding land use and development trends of different types of stations. Based on AFC data, a variety of effectiveness indicators were used to determine the number of classifications. By way of principal component analysis, k-means clustering and multiple linear regression, qualitative and quantitative analysis were combined to classify the sites. A total of 58 stations of Suzhou Rail Transit Line 1 and 2 were divided into 4 categories,which would lay the foundation for the follow-up study of the classified sites and the development of rail transit.
The classification of rail transit stations has an important impact on the study of passenger flow pat terns, surrounding land use and development trends of different types of stations. Based on AFC data, a variety of effectiveness indicators were used to determine the number of classifications. By way of principal component analysis, k-means clustering and multiple linear regression, qualitative and quantitative analysis were combined to classify the sites. A total of 58 stations of Suzhou Rail Transit Line 1 and 2 were divided into 4 categories,which would lay the foundation for the follow-up study of the classified sites and the development of rail transit.
引文
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[2] CERVERO R, DUNCAN M. Residential self selection and rail commuting:A nested logit analysis[C]//Cali fornia:University of California Transportation Center Working Papers,2002.
[3] CERVERO R, JIN M. Rail+property development:A model of sustainable transit finance and urbanism[C]//California:University of California Transportation Center Working Papers,2008.
[4] KUBY M,BARRANDA A, UPCHURCH C. Factors influencing light-rail station boardings in the united states[J]. Transportation Research Part A,2004,38(3):223-247.
[5]李向楠.城市轨道交通站点分类的聚类方法研究[J].铁道标准设计,2015(4):19-23.
[6]贺鑫,李科.基于聚类分析法的城市轨道交通站点分类[J].信息通信,2015(7):36-37.
[7]金昱.城市轨道交通站点客流时变特征及其影响因素研究———以上海为例[J].现代城市研究,2015(6):13-19.
[8] BAGCHI M,WHITE P R. The potential of public transport smart card data[J]. Transport Policy,2005,12(5):464-474.
[9] KIM K,OH K,LEE Y K,et al. Discovery of travel patterns in seoul metropolitan subway using big data of smart card transaction systems[J]. The Journal of Society for E-Business Studies,2013,18(3):211-222.
[10] KIM K W,LEE D W,CHUN Y H. A comparative study on the service coverages of subways and buses[J]. Ksce Journal of Civil Engineering,2010,14(6):915-922.
[11] DUNN J C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters[J]. Journal of Cybernetics,1973,3(3):32-57.
[12] XIE X L,BENI G. A validity measure for fuzzy clustering[J]. IEEE Trans Pami,1991,13(13):841-847.