基于刷卡数据的公共汽车客流网络复杂性日内变化研究

doi:10.12082/dqxxkx.2020.190576

Abstract

Abstract:

Public transportation in large cities is a typical complex giant system. The use of complex network methods to analyze large city public transport network systems is of great significance for urban transport development. Most of current studies take public transport stations as nodes and routes as connecting edges to construct an abstract network adjacency matrix, so to analyze the complexity characteristics of public transport networks according to indicators such as average (shortest) path length, clustering coefficient, degree distribution, node proximity or median centrality. However, the complexity of urban public transport network is not a static topology network composed of bus stops, routes, and their interconnections, but also the dynamic traffic information, which is seldom considered in existing studies. Distinguishing the characteristics of the passenger flow network in different time periods is of great value for formulating time-sharing public transport management policies. Meanwhile, the widely used big data recently provide high-precision traffic information for the study of dynamic traffic network structures. In this paper, we constructed the bidirectional adjacency matrix of passenger flows in different time periods based on swipe card data of Beijing buses, then compared and analyzed the within-day variation of the bus passenger flow network through complex network indexes. In terms of the structural characteristics, the bus passenger flow network in each time period had a small average shortest path and a large clustering coefficient, meaning a small-world network; the distribution of accumulation degree was fitted as an exponential distribution, which indicates that the bus passenger flow network did not have scale-free characteristics. The bus passenger flow distribution in each time period had an obvious distance attenuation rule, and it was mainly for short-distance travels below 10km, which suggests the bus line and operation management should focus on the distance range below 10 km. Degrees centricity and weighted degree centrality of the spatial pattern in different times presented an obvious core-edge character but changing over time; the weighted degree centrality in the top 10 nodes changed a lot, according to which dynamic public transport hubs should be considered in a precious and accurate traffic planning and management. Our findings provide a reference for public transport planning and management policies. In the future, more comprehensive passenger flow data should be used to explore the structural characteristics from a multi-scale perspective.

Key words: bus, big data, passenger flow distribution, within-day variation, spatial pattern, complex network, policy implications, Beijing

ZHAO Shaoya, YANG Xingdou, DAI Teqi, ZHANG Chao. Within-Day Variation of the Complexity of Bus Passenger Flow Network based on Smart Card Data[J].Journal of Geo-information Science, 2020, 22(6): 1254-1267.DOI:10.12082/dqxxkx.2020.190576

Figures/Tables 12

Tab. 1

Fig. 1

Tab. 2

Fig. 2

Tab. 3

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Tab. 4

Weighted centrality of the top 10 nodes for each time period on August 13, 2015, Beijing"

时段	加权度中心性	排名前10的节点
5:00—7:00	入	国贸、六里桥东、四惠、东直门、国贸东、天通苑北、六里桥北、燕莎桥南、北京西站、北京儿童医院
5:00—7:00	出	六里桥北、六里桥东、东直门、国贸东、北皋、国贸、四惠、东坝中路南口、东坝、大屯
7:00—9:00	入	国贸、四惠、国贸东、东直门、大屯、北京儿童医院、六里桥东、西直门、中关村、天通苑北
7:00—9:00	出	国贸、四惠、东直门、六里桥北、六里桥东、大屯、北京儿童医院、航天桥、马甸桥、西直门
9:00—11:00	入	国贸、中关村、东直门、四惠、国贸东、大屯、白石桥东、北京儿童医院、西直门、红庙路口
9:00—11:00	出	国贸、四惠、东直门、大屯、六里桥北、北京西站、红庙路口、六里桥东、西直门、国贸东
11:00—13:00	入	国贸、东直门、大屯、四惠、前门、北京西站、六里桥东、国贸东、北京儿童医院、六里桥北
11:00—13:00	出	国贸、东直门、四惠、北京西站、大屯、北京儿童医院、六里桥北、六里桥东、西直门、国贸东
13:00—15:00	入	国贸、东直门、大屯、四惠、北京西站、北京儿童医院、六里桥东、前门、六里桥北、国贸东
13:00—15:00	出	国贸、四惠、东直门、北京西站、大屯、六里桥东、北京儿童医院、木樨园桥、六里桥北、国贸东
15:00—17:00	入	国贸、东直门、四惠、北京西站、大屯、六里桥北、六里桥东、前门、牡丹园、西直门
15:00—17:00	出	国贸、东直门、四惠、北京西站、大屯、北京儿童医院、动物园、西直门、六里桥东、木樨园桥
17:00—19:00	入	六里桥北、四惠、国贸、大屯、东直门、六里桥东、牡丹园、通惠、西直门、八宝山
17:00—19:00	出	国贸、四惠、大屯、北京儿童医院、六里桥东、国贸东、东直门、北京西站、六里桥北、木樨园桥
19:00—21:00	入	四惠、国贸、六里桥北、大屯、通惠、东直门、六里桥东、北京西站、沙河、红庙路口
19:00—21:00	出	国贸、四惠、国贸东、东直门、中关村、天通苑北、六里桥东、大屯、北京西站、六里桥北
21:00—23:00	入	六里桥北、大屯、四惠、国贸、北京西站、北京西站、六里桥东、牡丹园、东直门、立水桥
21:00—23:00	出	国贸、中关村、四惠、国贸东、天通苑北、大屯、东直门、立水桥、北京西站、六里桥东

Tab. 4

Fig. 7

Tab. 5

References 36

[1]	Liu R, Chen Y, Wu J P, et al. Mapping spatial accessibility of public transportation network in an urban area: A case study of Shanghai Hongqiao transportation hub[J]. Transportation Research Part D: Transport and Environment, 2018,59:478-495.
[2]	吴康, 方创琳, 赵渺希. 中国城市网络的空间组织及其复杂性结构特征[J]. 地理研究, 2015,34(4):711-728.
	[ Wu K, Fang C L, Zhao M X. The spatial organization and structure complexity of Chinese intercity networks[J]. Geographical Research, 2015,34(4):711-728. ]
[3]	陈光, 温广辉, 虞文武. 基于复杂网络的城市公交网络研究综述[J]. 南京信息工程大学学报(自然科学版), 2018,10(4):401-408.
	[ Chen G, Wen G H, Yu W W. A survey of studies on urban public transportation networks based on complex network[J]. Journal of Nanjing University of Information Science & Technology (Natural Science Edition), 2018,10(4):401-408. ]
[4]	王姣娥, 莫辉辉, 金凤君. 中国航空网络空间结构的复杂性[J]. 地理学报, 2009,64(8):899-910.
	[ Wang J E, Mo H H, Jin F J. Spatial structural characteristics of chinese aviation network based on complex network theory[J]. Acta Geographica Sinica, 2009,64(8):899-910.]
[5]	段德忠, 刘承良, 杜德斌, 等. 基于二分网络的北京公交线路布局的空间依赖性[J]. 地理学报, 2016,71(12):2185-2198.
	[ Duan D Z, Liu C L, Du D B, et al. Spatial dependency of bus-line distribution based on bipartite network: A case study of Beijing city[J]. Acta Geographica Sinica, 2016,71(12):2185-2198. ]
[6]	唐秋生, 冯海峰, 李维. 基于复杂网络理论的长沙市公交网络拓扑性质研究[J]. 交通信息与安全, 2013,31(4):49-52.
	[ Tang Q S, Feng H F, Li W. Topology of Changsha public traffic network based on complex network theory[J]. Journal of Transport Information and Safety, 2013,31(4):49-52. ]
[7]	许晴, 祖正虎, 徐致靖, 等. 330个中国城市P空间下公交复杂网络实证研究[J]. 交通运输系统工程与信息, 2013,13(1):193-198.
	[ Xu Q, Zu Z H, Xu Z Q. Space P-based empirical research on public transport complex networks in 330 cities of China[J]. Journal of Transportation Systems Engineering and Information Technology, 2013,13(1):193-198. ]
[8]	钟少颖, 王宁宁, 陈锐. 北京市公交网络枢纽性和抗毁性——基于复杂网络分析方法的研究[J]. 城市发展研究, 2016,23(6):123-132.
	[ Zhong S Y, Wang N N, Chen R. The analysis of the hub and invulnerability of Beijing city bus transportation network based on complex network methods[J]. Urban Development Studies, 2016,23(6):123-132. ]
[9]	王姣娥, 焦敬娟, 黄洁, 等. 交通发展区位测度的理论与方法[J]. 地理学报, 2018,73(4):666-676.
	[ Wang J E, Jiao J J, Huang J, et al. Theory and methodology of transportation development and location measures[J]. ActaGeographicaSinica, 2018,73(4):666-676. ]
[10]	Malleson N. Building temporal dynamism into applied GIS research[J]. Applied Spatial Analysis and Policy, 2019,12(1):1-3.
[11]	王凯, 甘畅, 杨亚萍, 等. 长江中游城市群市域旅游经济网络结构演变及其驱动因素[J]. 地理与地理信息科学, 2019,35(5):118-125.
	[ Wang K, Gan C, Yang Y P, et al. Evolution and driving factors of urban tourism economic network structure in urban agglomeration in the middle reaches of the Yangtze River[J]. Geography and Geo-Information Science, 2019,35(5):118-125. ]
[12]	孟德友, 冯兴华, 文玉钊. 铁路客运视角下东北地区城市网络结构演变及组织模式探讨[J]. 地理研究, 2017,36(7):1339-1352.
	[ Meng D Y, Feng X H, Wen Y Z. Urban network structure evolution and organizational pattern in Northeast China from the perspective of railway passenger transport[J]. Geographical Research, 2017,36(7):1339-1352. ]
[13]	余红楚, 方志祥, 陆锋, 等. 重要经济发展区域间海运网络时空演变特性分析[J]. 地球信息科学学报, 2018,20(5):582-592.
	[ Yu H C, Fang Z X, Lu F, et al. Spatial-temporal evolution patterns of maritime networks between important economic developing zones revealed[J]. Journal of Geo-information Science, 2018,20(5):582-592. ]
[14]	刘帅宾, 杨山, 王钊. 基于人口流的中国省域城镇化空间关联特征及形成机制[J]. 地理学报, 2019,74(4):648-663.
	[ Liu B S, Yang S, Wang Z. Characteristics and formation mechanism of China's provincial urbanization spatial correlation based on population flow[J]. Acta Geographica Sinica, 2019,74(4):648-663.]
[15]	陈少沛. 城市地铁网络拓扑时空演变特征模型研究[J]. 地理空间信息, 2019,17(9):5-9.
	[ Chen S P. Research on spatio-temporal evolution characteristic model of urban subway network topology[J]. Geospatial Information, 2019,17(9):5-9. ]
[16]	龙瀛, 张宇, 崔承印. 利用公交刷卡数据分析北京职住关系和通勤出行[J]. 地理学报, 2012,67(10):1339-1352.
	[ Long Y, Zhang Y, Cui C Y. Identifying commuting pattern of Beijing using bus smart card data[J]. Acta Geographica Sinica, 2012,67(10):1339-1352. ]
[17]	孟斌, 黄松, 尹芹. 北京市居民地铁出行出发时间弹性时空分布特征研究[J]. 地球信息科学学报, 2019,21(1):107-117.
	[ Meng B, Huang S, Yin Q. Spatial and temporal distribution characteristics of residents' depart times elasticity in Beijing[J]. Journal of Geo-information Science, 2019,21(1):107-117. ]
[18]	刘瑜, 詹朝晖, 朱递, 等. 集成多源地理大数据感知城市空间分异格局[J]. 武汉大学学报·信息科学版, 2018,43(3):327-335.
	[ Liu Y, Zhan Z H, Zhu D, et al. Incorporating multi-source big geo-data to sense spatial heterogeneity pattern sina urban space[J]. Geomatics and Information Science of Wuhan University, 2018,43(3):327-335. ]
[19]	徐晓宇, 李梅. 基于开源大数据的北京地区餐饮业空间分布格局[J]. 地球信息科学学报, 2019,21(2):215-225.
	[ Xu X Y, Li M. Analysis on spatial distribution pattern of Beijing restaurants based on open source big data[J]. Journal of Geo-information Science, 2019,21(2):215-225. ]
[20]	尹芹, 孟斌, 张丽英. 基于客流特征的北京地铁站点类型识别[J]. 地理科学进展, 2016,35(1):126-134.
	[ Yin Q, Meng B, Zhang L Y. Classification of subway stations in Beijing based on passenger flow characteristics[J]. Progress in Geography, 2016,35(1):126-134. ]
[21]	赵梓渝, 魏冶, 杨冉, 等. 中国人口省际流动重力模型的参数标定与误差估算[J]. 地理学报, 2019,74(2):203-221.
	[ Zhan Z Y, Wei Y, Yang R, et al. Gravity model coefficient calibration and error estimation: Based on Chinese interprovincial population flow[J]. Acta Geographica Sinica, 2019,74(2):203-221. ]
[22]	徐敏, 黄震方, 曹芳东, 等. 基于大数据分析的城市旅游地网络结构特征及其演化模式——以新浪微博签到数据为例[J]. 地理研究, 2019,38(4):937-949.
	[ Xu M, Huang Z F, Cao F D, et al. The network structure of urban tourist destination and its evolution mode based on big data analysis: taking the data of Sina weibo sign-in as an example[J]. Geographical Research, 2019,38(4):937-949. ]
[23]	刘大均. 长江中游城市群旅游流空间格局及发展模式[J]. 经济地理, 2018,38(5):217-223.
	[ Liu D J. Spatial pattern and development model of tourist flow in urban agglomeration in the middle reaches of the Yangtze River[J]. Economic Geography, 2018,38(5):217-223. ]
[24]	黄爱玲, 关伟, 毛保华, 等. 北京公交线路客流加权复杂网络特性分析[J]. 交通运输系统工程与信息, 2013,13(6):198-204.
	[ Huang A L, Guan W, Mao B H, et al. Statistical analysis of weighted complex network in Beijing public transit routes system based on passenger flow[J]. Journal of Transportation Systems Engineering and Information Technology, 2013,13(6):198-204. ]
[25]	陈培文, 陈峰, 胡映月, 等. 基于复杂网络的城市轨道交通网络中心性研究[J]. 复杂系统与复杂性科学, 2017,14(2):97-102,109.
	[ Chen P W, Chen F, Hu Y Y, et al. On urban rail transit network centrality using complex network theory[J]. Complex Systems and Complexity Science, 2017,14(2):97-102,109. ]
[26]	冯佳, 许奇, 李夏苗, 等. 城市轨道交通系统网络复杂性研究[J]. 交通运输系统工程与信息, 2017,17(6):242-247.
	[ Feng J, Xu Q, Li X M, et al. Complex Network Study on Urban Rail Transit Systems[J]. Journal of Transportation Systems Engineering and Information Technology, 2017,17(6):242-247. ]
[27]	黄洁, 王姣娥, 靳海涛, 等. 北京市地铁客流的时空分布格局及特征——基于智能交通卡数据[J]. 地理科学进展, 2018,37(3):397-406.
	[ Huang J, Wang J E, Jin H T, et al. Investigating spatiotemporal patterns of passenger flows in the Beijing metro system from smart card data[J]. Progress in Geography, 2018,37(3):397-406. ]
[28]	Feng J, Li X M, Mao B H, et al. Weighted complex network analysis of the Beijing subway system: Train and passenger flows[J]. Physica A: Statistical Mechanics and its Applications, 2017,474:213-223.
[29]	莫辉辉, 王姣娥. 复杂交通网络: 结构、过程与机理.北京:经济管理出版社, 2012.
	[ Mo H H, Wang J E. Complex transport network: Structure, process & mechanism[M]. Beijing: Economy & Management Publishing House, 2012. ]
[30]	吴建军, 高自友, 孙会君, 等. 城市交通系统复杂性——复杂网络方法及其应用[M]. 北京: 科学出版社, 2010.
	[ Wu J J, Gao Z Y, Sun H J, et al. Complexity of urban traffic system--complex network method and its application[M]. Beijing, China: Science Press, 2010. ]
[31]	王静, 刘剑锋, 马毅林, 等. 北京市轨道交通车站客流时空分布特征[J]. 城市交通, 2013,11(6):18-27.
	[ Wang J, Liu Jian F, Ma Y L, et al. Temporal and spatial passenger flow distribution characteristics at rail transit stations in Beijing[J]. Urban Transport of China, 2013,11(6):18-27. ]
[32]	Gan Z X, Feng T, Wu Y T, et al. Station-based average travel distance and its relationship with urban form and land use: An analysis of smart card data in Nanjing City, China[J]. Transport Policy, 2019,79:137-154.
[33]	Fransen K, Neutens T, Farber S, et al. Identifying public transport gaps using time-dependent accessibility levels[J]. Journal of Transport Geography, 2015,48:176-187.
[34]	Boisjoly G, El-Geneidy A. Daily fluctuations in transit and job availability: A comparative assessment of time-sensitive accessibility measures[J]. Journal of Transport Geography, 2016,52:73-81.
[35]	王成金. 中国交通流的衰减函数模拟及特征[J]. 地理科学进展, 2009,28(5):690-696.
	[ Wang C J. Function simulation and regularity of distance decay of inter-urban traffic flow in China[J]. Progress in Geography, 2009,28(5):690-696. ]
[36]	陈卓, 金凤君, 杨宇, 等. 高速公路流的距离衰减模式与空间分异特征——基于福建省高速公路收费站数据的实证研究[J]. 地理科学进展, 2018,37(8):1086-1095.
	[ Chen Z, Jin F J, Yang Y, et al. Distance-decay pattern and spatial differentiation of expressway flow: An empirical study using data of expressway toll station in Fujian Province[J]. Progress in Geography, 2018,37(8):1086-1095. ]

卡号信息	卡号类型	刷卡次序	交易时间	线路号	车牌号	上车站点	下车站点
75487061	1	622	20150814073030	62026	87501	6	14
71755266	18	598	20150814080505	331	420099	18	22

时段	总量/万人	最大值/人	最小值/人	均值/人	方差
5：00—7:00	29.02	497	1	6.71	14.35
7：00—9:00	137.15	2223	1	18.99	46.01
9：00—11:00	74.46	1272	1	11.45	24.12
11：00—13:00	47.33	870	1	8.02	15.56
13：00—15:00	41.83	612	1	7.47	14.36
15：00—17:00	51.16	773	1	8.58	17.40
17：00—19:00	100.35	1849	1	14.74	34.00
19：00—21:00	75.09	1460	1	11.70	27.70
21：00—23:00	32.03	547	1	7.25	15.02

客流时段	a	b	R²	F	F_(α=_0.01）≈13.75
5:00—7:00	2.33	0.21	0.98	376.07	通过检验
7:00—9:00	2.27	0.21	0.99	826.48	通过检验
9:00—11:00	2.07	0.21	1.00	1717.13	通过检验
11:00—13:00	1.85	0.20	1.00	4813.44	通过检验
13:00—15:00	1.87	0.20	1.00	2630.49	通过检验
15:00—17:00	1.97	0.210	1.00	1535.94	通过检验
17:00—19:00	1.84	0.201	1.00	4893.22	通过检验
19:00—21:00	1.74	-0.19	1.00	1501.15	通过检验
21:00—23:00	2.91	-0.23	1.00	498.83	通过检验

时段	L	Lr	C	Cr
5:00—7:00	3.32	2.54	0.41	0.02
7:00—9:00	2.84	2.20	0.52	0.03
9:00—11:00	2.89	2.26	0.50	0.03
11:00—13:00	2.90	2.31	0.48	0.03
13:00—15:00	2.93	2.33	0.47	0.02
15:00—17:00	2.94	2.30	0.48	0.03
17:00—19:00	2.88	2.24	0.51	0.03
19:00—21:00	2.87	2.27	0.49	0.03
21:00—23:00	3.06	2.43	0.41	0.02

Within-Day Variation of the Complexity of Bus Passenger Flow Network based on Smart Card Data

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 12

References 36

Related Articles 15

Metrics

Comments

Recommended 0

[1]	LIU Yaxi, SONG Ci, LIU Qiyong, ZHANG Zhixin, WANG Xi, MA Jia, CHEN Xiao, PEI Tao. Spatial-temporal Characteristics of COVID-19 in Chongqing and Its Relationship with Human Mobility [J]. Journal of Geo-information Science, 2021, 23(2): 222-235.
[2]	WANG Xiaofan, FANG Zhixiang, ZHONG Haoyu, ZOU Xinyan. Periodic Optimization of Drug Purchase Needs on Site for Chronic Diseases under the Epidemic Prevention and Control [J]. Journal of Geo-information Science, 2021, 23(2): 307-317.
[3]	GU Haisuo, CHEN Peng, LI Huibo. Overview and Prospect for Spatial-Temporal Prediction of Crime [J]. Journal of Geo-information Science, 2021, 23(1): 43-57.
[4]	WANG Zhihua, YANG Xiaomei, ZHOU Chenghu. Geographic Knowledge Graph for Remote Sensing Big Data [J]. Journal of Geo-information Science, 2021, 23(1): 16-28.
[5]	SONG Guanfu, CHEN Yong, LUO Qiang, WU Mengyao. Development and Prospect of GIS Platform Software Technology System [J]. Journal of Geo-information Science, 2021, 23(1): 2-15.
[6]	ZHAO Pengjun, CAO Yushu. Identifying Metro Trip Purpose using Multi-source Geographic Big Data and Machine Learning Approach [J]. Journal of Geo-information Science, 2020, 22(9): 1753-1765.
[7]	CHEN Ruru, HU Zhongmin, LI Shenggong, GUO Qun. Assessment of Normalized Difference Vegetation Index from Different Data Sources in Grassland of Northern China [J]. Journal of Geo-information Science, 2020, 22(9): 1910-1919.
[8]	ZHANG Ya, LIU Jiping, ZHOU Liang, WANG Yong, LI Peng fei. Cluster Identification and Spatial Characteristics Analysis of Shunfeng Express Service Facilities based on the DBSCAN Algorithm in Beijing [J]. Journal of Geo-information Science, 2020, 22(8): 1630-1641.
[9]	LI Wan, NIU Lu, CHEN Hong, WU Hua. Robust Downscaling Method of Land Surface Temperature by Using Random Forest Algorithm [J]. Journal of Geo-information Science, 2020, 22(8): 1666-1678.
[10]	SONG Rong, HU Yecui. Comprehensive Zoning of Land Consolidation Potential in Cities of China and Optimization Pathways [J]. Journal of Geo-information Science, 2020, 22(7): 1522-1531.
[11]	YAO Kezhen, YUE Shuping. Study on Spatial Distribution of Modern Sweet Diet and its Impact Factors in China based on Big Data from Internet [J]. Journal of Geo-information Science, 2020, 22(6): 1202-1215.
[12]	SUN Wei, LIN Xiaona. The Spatial Distribution of Automobile Manufacturing Enterprises and its Influencing Factors in Liuzhou [J]. Journal of Geo-information Science, 2020, 22(6): 1216-1227.
[13]	LI Tao, WANG Jiaoe, HUANG Jie. Research on Travel Pattern and Network Characteristics of Inter-city Travel in China's Urban Agglomeration during the National Day Week based on Tencent Migration Data [J]. Journal of Geo-information Science, 2020, 22(6): 1240-1253.
[14]	DU Delin, HUANG Jie, WANG Jiaoe. Assessment of Smart City Development Status in China based on Multi-source Data [J]. Journal of Geo-information Science, 2020, 22(6): 1294-1306.
[15]	CHEN Fangmiao, HUANG Huiping, JIA Kun. Study on the Administration and Construction of Urban Agglomeration with Spatiotemporal Big Data: A Progress Review [J]. Journal of Geo-information Science, 2020, 22(6): 1307-1319.