基于客流特征的北京地铁站点类型识别

doi:10.18306/dlkxjz.2016.01.014

Abstract

Abstract:

In cities there is a high concentration of social and economic activities at subway stations. Different types of subway stations have different functions, as reflected in their regional characteristics, traffic function, and land use. Meaningful station classification helps to understand urban functional partition and evaluate rail transportation infrastructure development. Based on the usage data of subway passes, this study classified the subway stations with time series clustering. The result shows that (1) subway station passenger flows have clear temporal and spatial differences. It reflects the temporal and spatial differences of urban functional partitions; (2) this study uses a time series clustering method. By considering passenger flow characteristics, subway stations can be divided into residential-oriented stations, employment-oriented stations, spatial mismatched stations, mixed mainly residential-oriented stations, mixed mainly employment-oriented stations, mixed type stations, commerce- and attraction-oriented stations, and other stations; (3) Using traffic data at subway stations is an effective way to compare spatial behavior and physical space.

Key words: passenger flow characteristics, time series clustering, subway stations, Beijing

Qin YIN, Bin MENG, Liying ZHANG. Classification of subway stations in Beijing based on passenger flow characteristics[J].PROGRESS IN GEOGRAPHY, 2016, 35(1): 126-134.

Figures/Tables 17

Tab.1

Tab.2

Tab.3

Tab.4

Tab.5

Fig.1

Tab.6

Result of K-means clustering"

类别	名称	站点数量/个	站点名称
1类	居住导向型	13	常营、次渠南、稻田、巩华城、广阳城、回龙观、朱辛庄、回龙观东大街、篱笆房、良乡大学城西、生命科学园、沙河、沙河高教园
2类	就业导向型	4	复兴门、金台夕照、荣京东街、西二旗
3类	职住错位型	14	朝阳门、车公庄、东单、东四、国贸、海淀黄庄、呼家楼、知春路、雍和宫、立水桥、宋家庄、望京西、西直门、宣武门
4类	错位偏居住型	50	安河桥北、八宝山、八角游乐园、八里桥、北宫门、北苑、小红门、长阳、西红门、草房、慈寿寺、次渠、褡裢坡、俸伯、高米店北、公益西桥、枣园、育新、天通苑南、古城路、管庄、果园、黄村火车站、黄渠、角门西、九棵树、玉泉路、永泰庄、苏庄、旧宫、梨园、立水桥南、良乡南关、林萃桥、临河里、龙泽、亦庄桥、新宫、天通苑北、马家堡、南邵、苹果园、蒲黄榆、清源路、十里堡、石门、顺义、西苑、土桥、天通苑
5类	错位偏就业型	21	奥林匹克公园、白石桥南、车公庄西、东大桥、东四十条、知春里、西小口、万源街、阜成门、高碑店、国家图书馆、和平西桥、建国门、健德门、永安里、柳芳、苏州街、经海路、灵境胡同、荣昌东街、三元桥
6类	混合型	31	安定门、安华桥、安贞门、奥体中心、北土城、北苑路北、中关村、西土城、团结湖、磁器口、大葆台、大屯路东、大望路、大钟寺、灯市口、光熙门、五道口、望京、青年路、国展、花园桥、惠新西街北口、亮马桥、牡丹园、木樨地、魏公村、四惠、人民大学、南礼士路、农业展览馆、生物医药基地
7类	综合型	49	巴沟、北海北、北京大学东门、菜市口、传媒大学、崔各庄、长椿街、西四五棵松、动物园高米店南、郭公庄、海淀五路居、和平里北街、张自忠路、王府井、肖村、和平门、后沙峪、花梨坎、黄村西大街、惠新西街南口、亦庄文化园、万寿路、新街口、霍营、积水潭、金台路、劲松、军事博物馆、刘家窑、马泉营、圆明园、同济南路、孙河、南法信、南锣鼓巷、森林公园南门、上地、双井、双桥、四惠东、义和庄、通州北苑、太阳宫、陶然亭、天安门东、天安门西、天宫院
8类	其他型	13	北京南站、北京站、北新桥、崇文门、东直门、鼓楼大街、良乡大学城、西单、良乡大学城北、平安里、前门、芍药居、天坛东门

Tab.6

Tab.7

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

References 21

[1]	段德罡, 张凡. 2013. 土地利用优化视角下的城市轨道交通站点分类研究: 以西安地铁2号线为例[J]. 城市规划, 37(9): 39-45.
	[Duan D G, Zhang F.2013. Study on classification of urban rail transit stations from the perspective of land use optimization: A case study on Xi’an subway line 2[J]. City Planning Review, 37(9): 39-45.]
[2]	傅搏峰, 吴娇蓉, 陈小鸿. 2008. 郊区轨道站点分类方法研究[J]. 铁道学报, 30(6): 19-23. doi: 10.3321/j.issn:1001-8360.2008.06.004
	[Fu B F, Wu J R, Chen X H.2008. Method of classification of suburban rail transit station sites[J]. Journal of the China Railway Society, 30(6): 19-23.] doi: 10.3321/j.issn:1001-8360.2008.06.004
[3]	龚晓芳. 2009. 现代城市轨道交通站点地区规划研究[D]. 西安: 长安大学.
	[Gong X F. 2009. The planning research of metro rail transit station in modern city[D]. Xi’an, China: Chang’an University.]
[4]	马小毅, 金安, 刘明敏, 等. 2013. 广州市轨道交通客流特征分析[J]. 城市交通, 11(6): 35-42. doi: 10.3969/j.issn.1672-5328.2013.06.007
	[Ma X Y, Jin A, Liu M M, et al.2013. Rail transit passenger flow characteristics in Guangzhou[J]. Urban Transport of China, 11(6): 35-42.] doi: 10.3969/j.issn.1672-5328.2013.06.007
[5]	秦萧, 甄峰, 熊丽芳, 等. 2013. 大数据时代城市时空间行为研究方法[J]. 地理科学进展, 32(9): 1352-1361. doi: 10.11820/dlkxjz.2013.09.005
	[Qin X, Zhen F, Xiong L F, et al.2013. Methods in urban temporal and spatial behavior research in the big data era[J]. Progress in Geography, 32(9): 1352-1361.] doi: 10.11820/dlkxjz.2013.09.005
[6]	石瑜. 2014. 基于GIS的城市地铁站点布设研究[D]. 石家庄: 石家庄铁道大学.
	[Shi Y. 2014. Research on the urban metro station layout based on GIS[D]. Shijiazhuang, China: Shijiazhuang Tiedao University.]
[7]	宋辞, 裴韬. 2012. 基于特征的时间序列聚类方法研究进展[J]. 地理科学进展, 31(10): 1307-1317. doi: 10.11820/dlkxjz.2012.10.008
	[Song C, Pei T.2012. Research progress in time series clustering methods based on characteristics[J]. Progress in Geography, 31(10): 1307-1317.] doi: 10.11820/dlkxjz.2012.10.008
[8]	孙旭. 2009. 时间序列全局特征聚类分析方法及其应用[J]. 统计教育, (3): 55-59.
	[Sun X.2009. Analysis and application of the global characteristics cluster[J]. Statistical Thinktank, (3): 55-59.]
[9]	谭啸. 2012. 天津城市轨道交通站点周边土地利用优化研究[D]. 天津: 天津大学.
	[Tan X. 2012. Study on optimization of land-use surrounding the urban rail station in Tianjin[D]. Tianjin, China: Tianjin University.]
[10]	王静, 刘剑锋, 马毅林, 等. 2013. 北京市轨道交通车站客流时空分布特征[J]. 城市交通, 11(6): 18-27. doi: 10.3969/j.issn.1672-5328.2013.06.005
	[Wang J, Liu J F, Ma Y L, et al.2013. Temporal and spatial passenger flow distribution characteristics at rail transit stations in Beijing[J]. Urban Transport of China, 11(6): 18-27.] doi: 10.3969/j.issn.1672-5328.2013.06.005
[11]	王玉萍. 2011. 城市轨道交通客流预测与分析方法[D]. 西安: 长安大学.
	[Wang Y P. 2011. Study on urban rail transit passenger forecast and analysis[D]. Xi’an, China: Chang’an University.]
[12]	吴娇蓉, 毕艳祥, 傅博峰. 2007. 基于郊区轨道交通站点分类的客流特征和换乘系统优先级分析[J]. 城市轨道交通研究, (11): 23-28. doi: 10.3969/j.issn.1007-869X.2007.11.007
	[Wu J R, Bi Y X, Fu B F.2007. Characteristics of in passenger different suburban rail stations and the priority of transfer system[J]. Urban Mass Transit, (11): 23-28.] doi: 10.3969/j.issn.1007-869X.2007.11.007
[13]	谢屾. 2011. 城市轨道交通站点周边空间形态整合浅析: 以南京为例[D]. 南京: 南京大学.
	[Xie S. 2011. Study on morphological integration of space surrounding urban rail transit stations: A case study of Nanjing[D]. Nanjing, China: Nanjing University.]
[14]	余丽洁, 李岩, 陈宽民. 2014. 基于谱聚类的城市轨道站点分类方法[J]. 交通信息与安全, 32(1): 122-125, 129. doi: 10.3963/j.issn1674-4861.2014.01.025
	[Yu L J, Li Y, Chen K M.2014. Using spectral clustering for urban rail station classification[J]. Journal of Transport Information and Safety, 32(1): 122-125, 129.] doi: 10.3963/j.issn1674-4861.2014.01.025
[15]	张文彤, 董伟. 2013. SPSS统计分析高级教程[M]. 2版. 北京: 高等教育出版社.
	[Zhang W T, Dong W. 2013. SPSS tongji fenxi gaoji jiaocheng[M]. 2nd ed. Beijing, China: Higher Education Press.]
[16]	张育南. 2011. 城市轨道交通与城市空间整合发展问题初探[J]. 华中建筑, 29(10): 89-91. doi: 10.3969/j.issn.1003-739X.2011.10.024
	[Zhang Y N.2011. Basic research on development of URTintegrated with urban spaces[J]. Huazhong Architecture, 29(10): 89-91.] doi: 10.3969/j.issn.1003-739X.2011.10.024
[17]	Bates Jr E G.1978. A study of passenger transfer facilities[C]//Planning and design of rapid transit facilities. Washington, DC: Transportation Research Record.
[18]	Choi J, Lee Y G, Kim T, et al.2012. An analysis of metro ridership at the station-to-station level in Seoul[J]. Transportation, 39(3): 705-722. doi: 10.1007/s11116-011-9368-3
[19]	Hoseini M, Jabbari H, Naddafi K, et al.2013. Concentration and distribution characteristics of airborne fungi in indoor and outdoor air of Tehran subway stations[J]. Aerobiologia, 29(3): 355-363. doi: 10.1007/s10453-012-9285-8
[20]	Korf J L, Demetsky M J.1981. Analysis of rapid transit access mode Choice[C]//60th annual meeting of transportation research record, No. 817. Washington, DC: Transportation Research Record.
[21]	Vilcassim M J R, Thurston G D, Peltier R E, et al.2014. Black carbon and particulate matter (PM2.5) concentrations in New York City’s subway station[J]. Environmental Science & Technology, 48(24): 14738-14745. doi: 10.1021/es504295h pmid: 25409007

ID	进站名称	进站刷卡时间	出站名称	出站刷卡时间
10007510*****6142	长椿街	2013/3/1 8:03:00	复兴门	2013/3/1 8:10:26
10007510*****5723	苹果园	2013/3/1 8:12:00	玉泉路	2013/3/1 8:29:08
10007510*****2821	通州北苑	2013/3/1 8:28:00	军事博物馆	2013/3/1 9:25:23
10007510*****6032	和平门	2013/3/1 9:36:00	五棵松	2013/3/1 10:07:08
10007510*****5779	古城路	2013/3/1 11:10:00	王府井	2013/3/1 11:58:26

	总客流量/ (万/人次)	最大客流量/ (万/人次)	最小客流量/ (万/人次)	平均客流量/(万/人次)
进站	5383.38	467.11	357.71	384.53
出站	5503.56	483.29	365.45	393.11
合计	10886.94	-	-	777.64

站点名称	4:00前			4:00-5:00		……	21:00-22:00			22:00-23:00
站点名称	人数		标准化值		人数	标准化值	……	人数	标准化值	人数	标准化值
安华桥	0	-1.038		14	-0.992	……	58	-0.854		1	-1.035
八里桥	0	-0.712		75	-0.520		21	-0.657		2	-0.708
巴沟	3	-1.321		24	-1.258		97	-1.045		3	-1.320
北土城	1	-1.143		26	-1.088		106	-0.910		4	-1.137
草房	3	-0.753		268	-0.605		105	-0.696		3	-0.753

站点名称	4:00前			4:00-5:00		……	21:00-22:00			22:00-23:00
站点名称	人数		标准化值		人数	标准化值	……	人数	标准化值	人数	标准化值
安华桥	2	-0.874		48	-0.868	……	151	-0.495		1	-0.871
八里桥	1	-0.915		23	-0.911		238	-0.031		117	-0.482
巴沟	1	-1.269		88	-1.267		338	-0.508		109	-1.023
北土城	8	-1.070		117	-1.055		230	-0.615		60	-0.951
草房	0	-0.920		0	-0.920		1093	-0.170		357	-0.675

站定名称	形态特征			基本统计特征						客流的时间分布特征
	极大值点			偏度				峰度		客流时段分布均衡系数			高峰小时系数/%
	进站		出站			进站	出站		进站	出站	进站	出站	进站	出站
安华桥	3	3		1.34	2.46		0.87	7.38		3.523	4.419		16.69	24.94
八里桥	3	3		2.25	1.96		4.36	3.34		3.396	2.387		24.32	20.03
巴沟	4	4		0.70	1.09		-0.19	0.97		2.324	3.056		12.27	14.41
北土城	2	3		1.07	1.75		0.14	3.66		3.238	3.653		14.27	19.31
草房	3	2		2.25	1.93		4.48	3.22		3.294	2.396		23.02	20.75

Classification of subway stations in Beijing based on passenger flow characteristics

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 17

References 21

Related Articles 15

Metrics

Comments

Recommended 0

	聚类			误差		F	Sig.
	均方		df			均方	df
进站峰度	113.559	7		1.830	187	62.045	0
进站偏度	8.387	7		0.101	187	82.931	0
进站客流时段分布均衡系数	2.637	7		0.415	187	6.346	0
进站高峰小时系数	896.166	7		4.834	187	185.404	0
进站极大值个数	1.124	7		0.311	187	3.612	0.001
出站偏度	10.459	7		0.105	187	99.916	0
出站峰度	175.055	7		1.481	187	118.220	0
出站高峰小时系数	756.462	7		3.66	187	206.707	0
出站客流时段分布均衡系数	15.388	7		0.202	187	76.131	0
出站极大值个数	1.194	7		0.327	187	3.650	0.001

[1]	ZHU Guanyu, CHEN Peng. Spatial evolution and influencing factors of criminals’ cross-area co-offending network: An example of fraud in Beijing [J]. PROGRESS IN GEOGRAPHY, 2020, 39(5): 792-803.
[2]	GUO Yaqi, CHEN Peng. Formation and feature analyses of crime hotspots using near repeat principle: A case study of robbery in Beijing [J]. PROGRESS IN GEOGRAPHY, 2020, 39(5): 804-814.
[3]	ZHANG Feng, CHEN Yanguang, LIU Peng. Spatiotemporal relationships between urban system and water system in the Beijing-Tianjin-Hebei region [J]. PROGRESS IN GEOGRAPHY, 2020, 39(3): 377-388.
[4]	LU Minghua, ZHOU Yueyan, LIU Hanchu, XU Xin. Spatiotemporal dynamics and influencing factors of direct investment in Hebei Province by Beijing’s enterprises [J]. PROGRESS IN GEOGRAPHY, 2020, 39(3): 389-401.
[5]	LUO Kui, LI Guangdong, LAO Xin. Spatial reconstruction and optimization of industrial development in the Beijing-Tianjin-Hebei urban agglomeration [J]. PROGRESS IN GEOGRAPHY, 2020, 39(2): 179-194.
[6]	LI Xiaoli, SONG Weixuan, WU Wei, MA Yuzhu. Beijing urban spatial cognition based on follow-up investigations:Take graduate students in the University of Chinese Academy of Sciences as an example [J]. PROGRESS IN GEOGRAPHY, 2020, 39(2): 276-285.
[7]	LIU Xiaoxiao, WU Kang. Change of industrial investment networks in the central core area of the Beijing-Tianjin-Hebei region under the background of non-capital function dispersal [J]. PROGRESS IN GEOGRAPHY, 2020, 39(12): 1972-1984.
[8]	FU Hui, LIU Yanjun, SUN Hongri, ZHOU Guolei. Spatiotemporal characteristics and dynamic mechanism of cultivated land use transition in the Beijing-Tianjin-Hebei region [J]. PROGRESS IN GEOGRAPHY, 2020, 39(12): 1985-1998.
[9]	WANG Bei, WANG Liang, LIU Yanhua, YANG Bo, HUANG Xiaochun, YANG Ming. Characteristics of jobs-housing spatial distribution in Beijing based on mobile phone signaling data [J]. PROGRESS IN GEOGRAPHY, 2020, 39(12): 2028-2042.
[10]	NIU Qiang, SHEN Yingjie, ZHOU Yi, HUANG Jingnan, WANG Pan. Effect and lag period of urban agglomeration construction land growth on economic growth based on lagged variable model:A case study of the Beijing-Tianjin-Hebei urban agglomeration [J]. PROGRESS IN GEOGRAPHY, 2020, 39(10): 1656-1666.
[11]	Chunjiang LI, Jing MA, Yanwei CHAI, Mei-Po KWAN. Influence of neighborhood environment and noise pollution on residents’ mental health in Beijing [J]. PROGRESS IN GEOGRAPHY, 2019, 38(7): 1103-1110.
[12]	Xiaoxue SHI, Daoyi GONG, Yihong HU. Regional strong winter wind events over Beijing-Tianjin-Hebei and the associated atmospheric circulation changes during 1979-2017 [J]. PROGRESS IN GEOGRAPHY, 2019, 38(7): 1069-1079.
[13]	Lifan SHEN, Chun ZHANG, He LI, Ye WANG, Zijia WANG. Relationship between urban rail transit commuting and jobs-housing balance: An empirical analysis from Beijing based on big data methods [J]. PROGRESS IN GEOGRAPHY, 2019, 38(6): 791-806.
[14]	Bingchun LIU, Xin QI, Qingshan WANG. Urban metabolism prediction of Beijing City based on long short-term memory neural network [J]. PROGRESS IN GEOGRAPHY, 2019, 38(6): 851-860.
[15]	Xing HUANG, Yuting LIU. Exploring new-build gentrification in urban China from the demand side: A case of the ZS neighborhood in Xuanwumen, Beijing [J]. PROGRESS IN GEOGRAPHY, 2019, 38(4): 577-587.