基于地铁刷卡数据的城市通勤与就业中心吸引范围研究

doi:10.13959/j.issn.1003-2398.2017.03.012

PDF(6513 KB)

人文地理 ›› 2017, Vol. 32 ›› Issue (3) : 93-101. DOI: 10.13959/j.issn.1003-2398.2017.03.012

城市

基于地铁刷卡数据的城市通勤与就业中心吸引范围研究

许园园, 塔娜, 李响

作者信息 +

ANALYSIS OF COMMUTING BEHAVIOR AND EMPLOYMENT CENTER USING SUBWAY SMART CARD DATA

XU Yuan-yuan, TA Na, LI Xiang

Author information +

文章历史 +

摘要

地理时空大数据为通勤行为和城市空间结构研究带来了新的机遇。基于一周地铁刷卡数据，采用出行链（trip-chain）数据模型对用户一天的出行进行描述，建立通勤识别规则，识别出上海市域的职住空间和通勤格局。上海市域平均通勤时间为35 分钟，通勤主流向是向心流，说明上海市域依旧表现出强单中心结构。市域就业单中心与居住郊区化并存，中心城区多核心就业已经形成。在此基础上进一步分析中心城区13 个就业（次）中心的吸引范围、模式以及在整个市域的影响力，讨论多中心发展对职住平衡与通勤消减的意义。结果表明：多中心发展有利于城市通勤的合理分配，虽然次中心的平均通勤时间不一定减少，但是就业（次）中心已经表现出对邻近地区的通勤吸引和对主中心的通勤分流。

Abstract

With the development of information communication technologies, it becomes easier to access to the spatio-temporal data such as smart card data, GPS-enabled taxi data and social media data, which gives fresh impetus to research on commuting behavior and urban spatial structure. What's more, compared with the traditional data like questionnaires, this kind data has advantage of good continuity, wide coverage and dynamic update. At the same time, it is the impact of the urban space on commuting behavior that is the important topic of urban geography, urban planning and urban traffic study. And most people choose to travel by subway because of its convenience in many large cities worldwide. Therefore, this research aims to analyze commuting behavior and employment center based on subway smart card data.The subway smart card data used in this study was collected by the fare collection system for one week in April, 2015 in Shanghai. In this paper, we designedthe Trip-Chain data model corresponding to the passengers' travels in one day. According to the rules of identifying commuting built, wegraphically illustrated the spatial relation between residence and employment in Shanghai. Also the commuting pattern is depicted clearly.Meanwhilethe analysis outcomedemonstrates that the average commuting time in Shanghai is 35 minutes and the mainstream of commuting is centripetal. More interesting, the average commuting time increases concentrically from the inner city to the fringe area. In addition, the place of employment gathers in the core area while the place of residence in the suburbs. On the other hand, we identified the passenger-attracting scope of the 13 employmentcenters in the downtownseparately and they could be divided into three types: distance attenuation mode, enclave attracting mode and composite mode. Based on the number of the employment center's passenger-attracting, we summary all the passenger-attracting scopes in one map. And the identification results indicate the polycentric structure can adjust and decentralize the employment spatially, which improves the efficiency of the commuting integrally.

导出引用

许园园, 塔娜, 李响. 基于地铁刷卡数据的城市通勤与就业中心吸引范围研究[J]. 人文地理. 2017, 32(3): 93-101 https://doi.org/10.13959/j.issn.1003-2398.2017.03.012

XU Yuan-yuan, TA Na, LI Xiang. ANALYSIS OF COMMUTING BEHAVIOR AND EMPLOYMENT CENTER USING SUBWAY SMART CARD DATA[J]. HUMAN GEOGRAPHY. 2017, 32(3): 93-101 https://doi.org/10.13959/j.issn.1003-2398.2017.03.012

中图分类号： K901

参考文献

[1] 何舟,宋杰洁,孙斌栋.城市通勤时耗的空间结构影响因素——基于文献的研究与启示[J].城市规划学刊,2014(1):65-70. [He Zhou, Song Jiejie, Sun Bindong. Impact of urban spatial structure on urban commuting duration: Based on literature review[J]. Urban Planning Forum, 2014(1):65-70.]

[2] Gordon P, Kumar A, Richardson H. The influence of metropolitan spatial structure on commuting time[J]. Journal of Urban Economics, 1989,26(2):138-151.

[3] Cervero R, Wu K. Sub-centring and commuting: Evidence from the San Francisco Bay Area, 1980—1990[J]. Urban Studies, 1998,35(7): 1059-1076.

[4] Zhao P, Lü B, Roo G. Impact of the jobs-housing balance on urban commuting in Beijing in the transformation era[J]. Journal of Transport Geography, 2011,19(1):59-69.

[5] 周素红,闫小培.基于居民通勤行为分析的城市空间解读——以广州市典型街区为案例[J]. 地理学报,2006,61(2):179-189. [Zhou Suhong, Yan Xiaopei. The impact of commuters' travel pattern on urban structure: A case study in some typical communities in Guangzhou[J]. Acta Geographica Sinica, 2006,61(2):179-189.]

[6] 孙斌栋,石巍,宁越敏.上海市多中心城市结构的实证检验与战略思考[J]. 城市规划学刊,2010(1):58-63. [Sun Bindong, Shi Wei, Ning Yuemin. An empirical study on the polycentric urban structure of Shanghai and strategies in future[J]. Urban Planning Forum, 2010 (1):58-63.]

[7] 刘望保,侯长营.转型期广州市城市居民职住空间与通勤行为研究[J].地理科学,2014,34(3):272-279. [Liu Wangbao, Hou Changying. Urban resident's home-work space and commuting behavior in Guangzhou[J]. Scientia Geographica Sinica, 2014,34(3):272-279.]

[8] 孙斌栋,涂婷,石巍,等.特大城市多中心空间结构的交通绩效检验 ——上海案例研究[J].城市规划学刊,2013(2):63-69. [Sun Bindong, Tu Ting, Shi Wei, et al. Test on the performance of polycentric spatical structure as a measure of congestion reduction in megacities: The case study of Shanghai[J]. Urban Planning Forum, 2013 (2):63-69.]

[9] Levtnson D, Kumar A. Density and the journey to work[J]. Growth and Change, 1997,28(2):147-172.

[10] Schwanen T, Dijst M, Dieleman F. Policies for urban form and their impact on travel: The Netherlands experience[J]. Urban Studies, 2004,41(3):579-603.

[11] 丁成日.城市空间结构和用地模式对城市交通的影响[J].城市交通,2010,8(5):28-35. [Ding Chengri. The impact of urban spatial structure and land use pattern on urban transportation[J]. Urban Transport of China, 2010,8(5):28-35.]

[12] 钮心毅,丁亮,宋小冬.基于手机数据识别上海中心城的城市空间结构[J]. 城市规划学刊,2014(6):61-67. [Niu Xinyi, Ding Liang, Song Xiaodong. Understanding urban spatial structure of Shanghai central city based on mobile phone data[J]. Urban Planning Forum, 2014(6):61-67.]

[13] 丁亮,钮心毅,宋小冬.利用手机数据识别上海中心城的通勤区[J]. 城市规划,2015,39(9):100-106. [Ding Liang, Niu Xinyi, Song Xiaodong. Identifying the commuting area of Shanghai central city using mobile phone data[J]. City Planning Review, 2015,39(9):100-106.]

[14] 周素红,郝新华,柳林.多中心化下的城市商业中心空间吸引衰减率验证——深圳市浮动车GPS时空数据挖掘[J].地理学报,2014, 69(12):1810-1820. [Zhou Suhong, Hao Xinhua, Liu Lin. Validation of spatial decay law caused by urban commercial certer's mutual attraction in polycentric city: Spatio-temporal data mining of floating cars' GPS data in Shenzhen[J]. Acta Geographica Sinica, 2014,69(12):1810-1820.]

[15] 许宁,尹凌,胡金星.从大规模短期规则采样的手机定位数据中识别居民职住地[J].武汉大学学报(信息科学版),2014,39(6):750-756. [Xu Ning, Yin Ling, Hu Jinxing. Indentifying home-work locations from short-term, large-scale, and regularly sampled mobile phone tracking data[J]. Geomatics and Information Science of Wuhan University, 2014,39(6):750-756.]

[16] 王波,甄峰,魏宗财.南京市区活动空间总体特征研究——基于大数据的实证分析[J].人文地理,2014,29(3):14-21. [Wang Bo, Zhen Feng, Wei Zongcai. The research on characteristics of urban activity space in Nanjing: An empirical analysis based on big data[J]. Human Geography, 2014,29(3):14-21.]

[17] 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]. Scientia Geographica Sinica, 2012,67(10):1339-1352.]

[18] Zhou J, Murphy E, Long Y. Commuting efficiency in the Beijing metropolitan area: An exploration combining smartcard and travel survey data[J]. Journal of Transport Geography, 2014,41:175-183.

[19] Long Y, Thill J. Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing[J]. Computers, Environment and Urban Systems, 2015,53:19-35.

[20] 董志国.上海轨道交通出行方式链模型研究[J].城市轨道交通研究,2012,15(7):15-21. [Dong Zhiguo. On the trip chain model of Shanghai urban rail transit[J]. Urban Mass Transit, 2012,15(7):15-21.]

[21] 刘天顺,马兴峰.上海地铁三号线乘客调查的思考[J].铁道运输与经济,2006,28(3):13-14. [Liu Tianshun, Ma Xingfeng. The investigation of Shanghai Metro Line Three[J]. Railway Transport and Economy, 2006,28(3):13-14.]

[22] 王雯静,干宏程.小汽车与轨道交通出行方式选择行为分析[J].城市交通,2010,8(3):36-40. [Wang Wenjing, Gan Hongcheng. Car or public transit? Analysis on travel mode choice behavior[J]. Urban Transport of China, 2010,8(3):36-40.]

[23] 高向东,江取珍.对上海城市人口分布变动与郊区化的探讨[J].城市规划,2002,26(1):66-69. [Gao Xiangdong, Jiang Quzhen. The redistribution of the population and suburbanization in Shanghai municipality[J]. City Planning Review, 2002,26(1):66-69.]

[24] 李健,宁越敏.1990 年代以来上海人口空间变动与城市空间结构重构[J].城市规划学刊,2007(2):20-24. [Li Jian, Ning Yuemin. Population spatial change and urban spatial restructuring in Shanghai since the 1990s[J]. Urban Planning Forum, 2007(2):20-24.]

[25] 潘海啸,陈国伟.轨道交通对居住地选择的影响——以上海市的调查为例[J].城市规划学刊,2009(5):71-76. [Pan Haixiao, Chen Guowei. Rail transit and residential location choice[J]. Urban Planning Forum, 2009(5):71-76.]

[26] 林涛.郊区新城发展与大城市空间结构调整:松江案例[J].人文地理,2010,25(5):75-80. [Lin Tao. Suburban new town development and its impacts on the metropolis spatial structure: A case study of Songjiang, Shanghai[J]. Human Geography, 2010,25(5):75-80.]

[27] McMillen D. Nonparametric employment subcenter identification[J]. Journal of Urban Economics, 2001,50(3):448-473.

[28] Craig S, NgPin T. Using quantile smoothing splines to identify employment subcenters in a multicentric urban area[J]. Journal of Urban Economics, 2001,49(1):100-120.

[29] McMillen D. Identifying sub-centres using contiguity matrices[J]. Urban Studies, 2003,40(1):57-69.

[30] 王波,甄峰.南京市区就业空间布局研究[J].人文地理,2011,26(4): 58-65. [Wang Bo, Zhen Feng. An analysis of spatial distribution of urban employment in Nanjing[J]. Human Geography, 2011,26(4): 58-65.]

[31] 秦波,王新峰.探索识别中心的新方法——以上海生产性服务业空间分布为例[J].城市发展研究,2010,17(6):43-48. [Qin Bo, Wang Xinfeng. Exploring a new approach to define urban center: A study on producer services distribution in Shanghai[J]. Urban Studies, 2010,17(6):43-48.]

[32] 孙斌栋,魏旭红.上海都市区就业—人口空间结构演化特征[J].地理学报,2014,69(6):747-758. [Sun Bindong, Wei Xuhong. Spatial distribution and structure evolution of employment andpopulation in Shanghai metropolitan area[J]. Acta Geographica Sinica, 2014,69 (6):747-758.]

[33] 曾海宏,孟晓晨,李贵才.深圳市就业空间结构及其演变(2001— 2004)[J].人文地理,2010,25(3):34-40.[Zeng Haihong, Meng Xiaochen, Li Guicai. Spatial structure of employment and its evolution in Shenzhen city: 2001—2004[J]. Human Geography, 2010,25(3): 34-40.]

[34] 涂婷.多中心城市结构与通勤效率的关系研究[D].上海:华东师范大学,2010. [Tu Ting. Study on Relationship of Ploy-centric Structure and Commuting Efficiency[D]. Shanghai: East China Normal University, 2010.]

[35] 丁亮,钮心毅,宋小冬.上海中心城就业中心体系测度——基于手机信令数据的研究[J].地理学报,2016,71(3):484-499. [Ding Liang, Niu Xinyi, Song Xiaodong. Measuring the employment center system in Shanghai central city: A study using mobile phone signaling data[J]. Acta Geographica Sinica, 2016,71(3):484-499.]