利用公交刷卡数据分析北京职住关系和通勤出行

doi:10.11821/xb201210005

摘要/Abstract

摘要： 基于位置服务(Location Based Service, LBS) 技术为研究城市系统的时空动态规律提供了新的视角, 已往多基于移动通讯(GSM)、全球定位系统(GPS)、社会化网络(SNS) 和无线宽带热点(Wi-Fi) 数据开展研究, 但少有研究利用公交IC 卡刷卡数据进行城市系统分析。普遍存在的LBS数据虽然具有丰富的时间和空间信息, 但缺乏社会维度信息, 使其应用范围受到一定限制。本文基于2008 年北京市连续一周的公交IC 卡(Smart Card Data, SCD) 刷卡数据, 结合2005 年居民出行调查、地块级别的土地利用图, 识别公交持卡人的居住地、就业地和通勤出行, 并将识别结果在公交站点和交通分析小区(TAZ) 尺度上汇总:① 将识别的通勤出行分别从通勤时间和距离角度, 与居民出行调查数据和其他已有北京相关研究进行对比, 显示较好的吻合性;② 对来自3 大典型居住区和去往6 大典型办公区的通勤出行进行可视化并对比分析;③ 对全市基于公交的通勤出行进行可视化, 并识别主要交通流方向。本研究初步提出了从传统的居民出行调查和城市GIS 数据建立规则, 用于SCD数据挖掘的方法, 具有较好的可靠性。

关键词: 职住关系, 通勤时间和距离, 空间错位, 北京, 公交IC 卡刷卡数据

Abstract: This paper combines the one-week bus smart card data (SCD) and one-day household travel survey as well as the parcel-level land use map for identifying jobs-housing places and commuting trips in the Beijing Metropolitan Area with an area of 16,410 square kilometers. The identification result is aggregated in the bus stop and traffic analysis zone (TAZ) levels, respectively. In particular, commuting trips with commuting time and distance attached from three typical residence communities and those to five typical business zones are mapped and compared with each other to analyze commuting patterns of Beijing. The identified commuting trips are compared with those in the household travel survey in terms of commuting time and distance, indicating that our results are coincident with the survey significantly. Our approach is proved to have its potential in identifying more solid identification result based on rules extracted from existing surveys or censuses.

Key words: bus smart card data, jobs-housing, commuting time and distance, spatial mismatch, Beijing

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 Ying, ZHANG Yu, CUI Chengyin. Identifying Commuting Pattern of Beijing Using Bus Smart Card Data[J]. Acta Geographica Sinica, 2012, 67(10): 1339-1352.

参考文献

[1] Batty M. Invisible cities. Environment and Planning B: Planning and Design. 1990, 17: 127-130, doi: 10.1068/b170127.

[2] Ahas R, Mark Uë. Location based services: New challenges for planning and public administration? Futures, 2005, 37:547-561, doi: 10.1016/j.futures.2004.10.012.

[3] Goodchild M F. Citizens as sensors: The world of volunteered geography. GeoJournal, 2007, 69(4): 211-221, doi:10.1007/s10708-007-9111-y.

[4] Newhaus F. Urban diary: A tracking project//UCL Working Paper Series. Paper 151.Available on line: http://discovery.ucl.ac.uk/19245/

[5] Jiang B, Yin J, Zhao S. Characterizing human mobility patterns in a large street network, Physical Review, 2009, E80021136, doi: 10.1103/PhysRevE.80.021136.

[6] Liu L, Andris C, Ratti C. Uncovering cabdrivers' behavior patterns from their digital traces Computers. Environmentand Urban Systems, 2010, 34(6): 541-548, doi: 10.1016/j.compenvurbsys.2010.07.004.

[7] Ratti C, Pulselli R M, Williams S et al. Mobile landscapes: Using location data from cell phones for urban analysis.Environment and Planning B: Planning and Design, 2006, 33(5): 727-748, doi: 10.1068/b32047.

[8] Calabrese F, Ratti C. Real time Rome. Networks and Communication Studies, 2006, 20: 247-258.

[9] Steenbruggen J, Borzacchiello M T, Nijkamp P et al. Mobile phone data from GSM networks for traffic parameter andurban spatial pattern assessment: A review of applications and opportunities. GeoJournal, 2011, doi: 10.1007/s10708-011-9413-y.

[10] Reades J, Calabrese F, Ratti C. Eigenplaces: Analysing cities using the space-time structure of the mobile phonenetwork. Environment and Planning B: Planning and Design, 2009, 36(5): 824-836, doi: 10.1068/b34133t.

[11] Milton R. Twitter tags and real-time visualisation of complex geographic data with MapTube. Advances in SpatialAnalysis & e-Social Science, 2010. A One Day CASA Conference, UCL, UK.Available on line: http://www.casa.ucl.ac.uk/conference/programme.html?abstract=a1500

[12] Jones B W, Spigel B, Malecki E J. Blog links as pipelines to buzz elsewhere: The case of New York theater blogs.Environment and Planning B: Planning and Design, 2010, 37(1): 99-111, doi: 10.1068/b35026.

[13] Rekimoto J, Miyaki T, Ishizawa T. LifeTag: WiFi-based continuous location logging for life pattern analysis. 3rdInternational Symposium on Location- and Context-Awareness (LOCA2007), 2007: 35-49.

[14] Torrens P M. Wi-Fi geographies. Annals of the Association of American Geographers, 2008, 98(1): 59-84, doi:10.1080/00045600701734133.

[15] Shoval N. Sensing human society. Environment and Planning B: Planning and Design, 2007, 34(2): 191-195, doi:10.1068/b3402com.

[16] Liu Zhilin, Wang Maojun. Job accessibility and its impacts on commuting time of urban residents in Beijing: From aspatial mismatch perspective. Acta Geographica Sinica, 2011, 66(4): 457-467. [刘志林, 王茂军. 北京市职住空间错位对居民通勤行为的影响分析: 基于就业可达性与通勤时间的讨论. 地理学报, 2011, 66(4): 457-467.]

[17] Long Y, Shen Z, Mao Q et al. Retrieving individuals' attributes from aggregated dataset for urban micro-simulation: Aprimary exploration. Geoinformatics, 2010, doi: 10.1109/GEOINFORMATICS.2010.5567738.

[18] Bagchi M, White P. What role for smart-card data from bus systems? Municipal Engineer 2004, 157(1): 39-46, doi:10.1680/muen.2004.157.1.39.

[19] Joh C H, Hwang C. A Time-geographic analysis of trip trajectories and land use characteristics in Seoul metropolitanarea by using multidimensional sequence alignment and spatial analysis, 2010 AAG Annual Meeting, Washington, DC.Abstract available on line:http://meridian.aag.org/callforpapers/program/AbstractDetail.cfm?AbstractID=30812

[20] Jang W. Travel time and transfer analysis using transit smart card data: Transportation Research Record. Journal of theTransportation Research Board, 2010, 2144: 142-149, doi: 10.3141/2144-16.

[21] Roth C, Kang S M, Batty M et al. Structure of urban movements: Polycentric activity and entangled hierarchical flowsPLoS ONE, 2011, 6(1): e15923, doi: 10.1371/journal.pone.0015923.

[22] Zhou Tao, Zhai Changxu, Gao Zhigang. Approaching bus OD matrices based on data reduced from bus IC cards.Urban Transport of China, 2007, 5(3): 48-52. [周涛, 翟长旭, 高志刚. 基于公交IC 卡数据的OD推算技术研究. 城市交通, 2007, 5(3): 48-52.]

[23] Dong Xiaojing, Yu Zhiwei, Fu Weiwei et al. Data processing and analyzing system for bus IC card based on GIS.Geospatial Information, 2009, 7(5): 124-126. [董晓晶, 余志伟, 伏伟伟等. 基于GIS 的公交IC 卡数据处理及分析系统. 地理空间信息, 2009, 7(5): 124-126.]

[24] Peng Han, Han Xiuhua, Tian Zhenzhong et al. Transfer matrix construction method based on bus IC card dataprocessing. Computer and Communications, 2007, (4): 32-34. [彭晗, 韩秀华, 田振中等. 公交IC 卡数据处理的换乘矩阵构造方法研究. 交通与计算机, 2007, (4): 32-34.]

[25] Yang Zhiwei, Zhao Qian, Zhao Shengchuan et al. Passenger flow volume forecasting method based on public transitintelligent card (IC) survey data. Transport Standardization, 2009, (9): 115-119. [杨智伟, 赵骞, 赵胜川等. 基于公交IC 卡数据信息的客流预测方法研究. 交通标准化, 2009, (9): 115-119.]

[26] Liu Xiaoming. Bus smart cards will be extensively adopted in Beijing on April 1, 2006, 2009, http://www.enet.com.cn/article/2005/1229/A20051229488080.shtml. accessed on 2010-05-25. [刘小明. 2006 年4 月1 日起北京将全面推行公交IC 卡. http://www.enet.com.cn/article/2005/1229/A20051229488080.shtml.]

[27] Beijing Transportation Research Center. Beijing Transportation Annual Report 2009. 2009. [北京市交通发展研究中心. 2009 北京市交通发展年度报告. 2009.]

[28] Beijing Institute of City Planning. Beijing Job Position Allocation for 2008. 2010. [北京市城市规划设计研究院. 2008年北京市就业空间分布研究报告. 2010.]

[29] Beijing Municipal Commission of Transport, Beijing Transportation Research Center. The 3rd TransportationComprehensive Survey Report of Beijing Working Report. 2007. [北京市交通委员会, 北京市交通发展研究中心. 北京市第三次交通综合调查总报告. http://210.75.218.99/InfoCenter/Func/OpenNews.asp?NewsID=INN20090706001.2007.]

[30] Zhao P, Lv B, Roo G de. Impact of the jobs-housing balance on urban commuting in Beijing in the transformation era.Journal of Transport Geography, 2011, 19: 59-69, doi: 10.1016/j.jtrangeo.2009.09.008.

[31] Wang D, Chai Y. The jobs-housing relationship and commuting in Beijing, China: The legacy of Danwei. Journal ofTransport Geography, 2009, 17: 30-38, doi: 10.1016/j.jtrangeo.2008.04.005.

[32] Jiang B, Liu X. Scaling of geographic space from the perspective of city and field blocks and using volunteeredgeographic information. International Journal of Geographical Information Science 2012, 26(2): 215-229, doi: 10.1080/13658816.2011.575074.