基于GPS数据的城市居民通勤弹性研究——以北京市郊区巨型社区为例

doi:10.11821/xb201206002

摘要/Abstract

摘要： 通勤是居民出行行为的重要组成部分,受到地理、规划、交通等领域的广泛关注,已有对通勤的研究多利用问卷调查数据,定位技术与信息通信技术为个体行为时空数据的采集带来了新的契机。本研究关注个体在不同工作日中通勤的可变性,将活动弹性的概念引入对通勤行为的研究中,提出通勤弹性的概念,并界定了时间、空间、方式、路径4 个通勤弹性维度,通过探讨4 种弹性之间的相互作用关系,提出7 种基于弹性的理论通勤模式。研究以北京市天通苑与亦庄两个郊区巨型社区为案例,基于活动日志与GPS 定位数据相结合的为期一周的居民时空行为数据,分别利用传统方法和通勤弹性视角研究居民的通勤特征,验证通勤弹性现象的存在以及该视角透视城市居民通勤行为的合理性,并利用GIS 三维可视化技术对7 种理论通勤模式居民的活动—移动时空特征进行刻画,从而透视北京市郊区巨型社区居民的通勤特征及复杂模式,为北京市城市与交通问题的解决提供了独特的视角。

关键词: GPS, 通勤弹性, 通勤模式, 三维可视化, 北京

Abstract: Commuting is one of the most important parts of travel behavior, which has been widely studied by geographers, planners and transportation researchers based on questionnaire data, and the rapid development of location-based technologies and ICTs bring new opportunities for deep insight of commuting with tracking data. The study focuses on variability in commuting behavior in different weekdays, introduces the concept of activity flexibility and brings forward a concept of commuting flexibility. Four dimensions, i.e., time, space, travel mode and route choice, are used to define the concept of commuting flexibility, and seven theoretical commuting patterns are put forward according to discussion on the relationship between four dimensions of commuting flexibility. The study takes two suburban mega-communities in Beijing, i.e., Tiantongyuan and Yizhuang, as study cases. It collects seven days of space-time data of individual behaviors through a web-based survey, which is a combination of activity-travel dairies and GPS tacking data. On the basis of the analysis of commuting behavior, the study explores measures and characteristics of four types of commuting flexibility and represents the activity-travel trajectories in a 3-D GIS environment. The concept of commuting flexibility proves to be effective and reasonable for a better understanding of complicated commuting behaviors of Beijing's residents. The study also provides a special perspective for Beijing's urban and transportation problems.

Key words: GPS, commuting flexibility, commuting pattern, 3-D visualization, Beijing

申悦, 柴彦威. 基于GPS数据的城市居民通勤弹性研究——以北京市郊区巨型社区为例[J]. 地理学报, 2012, 67(6): 733-744.

SHEN Yue, CHAI Yanwei. Study on Commuting Flexibility of Residents Based on GPS Data: A Case Study of Suburban Mega-communities in Beijing[J]. Acta Geographica Sinica, 2012, 67(6): 733-744.

参考文献

[1] Horner M. Spatial dimensions of urban commuting: A review of major issues and their implications for futuregeographic research. The Professional Geographer, 2004, 56(2): 160-173.
[2] Lowe J C. Patterns of spatial dispersion in metropolitan commuting. Urban Geography, 1998, 19(3): 232-253.
[3] Sohn J. Are commuting patterns a good indicator of urban spatial structure? Journal of Transport Geography, 2005, 13(4): 306-317.
[4] Fagnani J. Women's commuting patterns in the Paris region. Tijdschrift voor Economische en Sociale Geografie, 1983,(1): 12-24.
[5] Lee B S, McDonald J F. Determinants of commuting time and distance for Seoul residents: The impact of family statuson the commuting of women. Urban Studies, 2003, 40(7): 1283-1302.
[6] Peng Z R. The jobs-housing balance and urban commuting. Urban Studies, 1997, 34(8): 1215-1235.
[7] Schwanen T, Mokhtarian P L. What affects commute mode choice: Neighborhood physical structure or preferencestoward neighborhoods? Journal of Transport Geography, 2005, 13: 83-99.
[8] Shen Q. Spatial and social dimensions of commuting. Journal of the American Planning Association, 2000, 66(1): 68-82.
[9] Cao X, Mokhtarian P L. How do individuals adapt their personal travel? A concept exploration of the consideration oftravel-related strategies. Transport Policy, 2005, (12): 199-206.
[10] Horner M. Extensions to the concept of excess commuting. Environment and Planning A, 2002, 34(3): 543-566.
[11] Wang D G, Chai Y W. The jobs-housing relationship and commuting in Beijing, China: The legacy of Danwei.Transport Geography, 2009, 17: 30-38.
[12] Zhang Yan, Chai Yanwei. Characteristics of commuting pattern in Beijing: Based on the comparison of different urbanresidential areas. Geographical Research, 2009, 28(5): 1327-1340. [张艳, 柴彦威. 基于居住区比较的北京城市通勤研究. 地理研究, 2009, 28(5): 1327-1340.]
[13] Zhou Suhong, Yan Xiaopei. The impact of commuters' travel pattern on urban structure: A case study in some typicalcommunities in Guangzhou. Acta Geographica Sinica, 2006, 61(2): 179-189. [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. 地理学报, 2006, 61(2): 179-189.]
[14] Sun Bindong, Pan Xin, Ning Yuemin. Analysis on influence of job-housing balance on commute travel in Shanghai.Urban Planning Forum, 2008, (1): 77-82. [孙斌栋, 潘鑫, 宁越敏. 上海市就业与居住空间均衡对交通出行的影响分析. 城市规划学刊, 2008, (1): 77-82.]
[15] Chai Yanwei, Liu Zhilin, Li Zhengrong et al. The Time-space Structure of Chinese Cities. Beijing: Peking UniversityPress, 2002. [柴彦威, 刘志林, 李峥嵘等. 中国城市的时空间结构. 北京: 北京大学出版社, 2002.]
[16] Jones P, Clarke M. The significance and measurement of variability in travel behavior. Transportation, 1988, 15: 65-87.
[17] Huff J O, Hanson S. Repetition and variability in urban travel. Geographical Analysis, 1986, 18(2): 97-113.
[18] Pas E I. Intra-personal variability and model goodness-of-fit. Transportation Research, 1987, 21A(6): 431-438.
[19] Kitamura R, Yamamoto T, Susilo Y O et al. How routine is a routine? An analysis of the day-to-day variability inprism vertex location. Transportation Research Part A, 2006, 40(3): 259-279.
[20] Buliung R N, Roorda M J, Remmel T K. Exploring spatial variety in patterns of activity-travel behaviour: Initialresults from the Toronto Travel-Activity Panel Survey. Transportation, 2008, 35(6): 697-722.
[21] Shoval N. Tracking technologies and urban analysis. Cities, 2008, 25(1): 21-28.
[22] Stopher P, FitzGerald C, Xu M. Assessing the accuracy of the Sydney Household Travel Survey with GPS.Transportation, 2007, 34: 723-741.
[23] Badland H M, Duncan M J, Oliver M et al. Examining commute routes: Applications of GIS and GPS technology.Environmental Health and Preventive Medicine, 2010, 15(5): 327-330.
[24] Beckxa C, Panisa L I, Janssens D et al. Applying activity-travel data for the assessment of vehicle exhaust emissions:Application of a GPS-enhanced data collection tool. Transportation Research Part D, 2010, 15(2): 117-122.
[25] Ahas R, Mark U. Location based services: New challenges for planning and public administration? Futures, 2005, 37(6): 547-561.
[26] Ratti C, Frenchma D, Pulselli R M 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.
[27] González M C, Hidalgo C A, Barabási A L. Understanding individual human mobility patterns. Nature, 2008, 453:779-782.
[28] Licoppe C, Diminescu D, Smoreda Z et al. Using mobile phone geolocalisation for 'socio-geographical' analysis ofco-ordination, urban mobilities, and social integration patterns. Tijdschrift voor economische en sociale geografie,2008, 99(5): 584-601.
[29] Huang Xiaoting, Chai Yanwei, Zhao Ying et al. On the application analysis of mobile data source in the study oftourists. Tourism Tribune, 2010, 25(8): 39-45. [黄潇婷, 柴彦威, 赵莹等. 手机移动数据作为新数据源在旅游者研究中的应用探析. 旅游学刊, 2010, 25(8): 39-45.]
[30] Liu Zhilin, Zhang Yan, Chai Yanwei. Home-work separation in the context of institutional and spatial transformation in urban China: Evidence from Beijing household survey data. Urban Studies, 2009, 16(9): 123-130. [刘志林, 张艳, 柴彦威. 中国大城市职住分离现象及其特征: 以北京市为例. 城市发展研究, 2009, 16(9): 123-130.]
[31] Wang E, Song J, Xu T. From "spatial bond" to "spatial mismatch": An assessment of changing jobs-housingrelationship in Beijing. Habitat International, 2011, 35: 398-409.
[32] Hagerstrand T. What about people in regional science? Paper and Proceedings of the Regional Science Association,1970, 24: 7-21.
[33] Schwanena T, Kwan M P, Ren F. How fixed is fixed? Gendered rigidity of space-time constraints and geographies ofeveryday activities. Geoforum, 2008, 39(6): 2109-2121.
[34] Arentze T, Timmermans H. A Learning-Based Transportation Oriented Simulation System. Transportation ResearchPart B, 2004, 38(7): 613-633.
[35] Schwanen T, Dijst M. Time windows in workers' activity patterns: Empirical evidence from the Netherlands.Transportation, 2003, 30(3): 261-283.
[36] Doherty S T. Should we abandon activity type analysis? Redefining activities by their salient attributes. Transportation,2006, 33(6): 517-536.
[37] Yi Chengdong, Huang Youqin. Additional residences away from home: an empirical analysis on spatial relationship ofmultiple residences in Beijing. Economic Geography, 2011, 31(3): 396-403. [易成栋, 黄友琴. 家外有宅: 北京市家庭多套住宅的空间关系研究. 经济地理, 2011, 31(3): 396-403.]
[38] Miller H J. Tober's first law and spatial analysis. Annals of the Association of American Geographers, 2004, 94(2):284-289.
[39] Kwan M P. GIS Methods in Time-Geographic Research: Geocomputation and Geovisualization of Human ActivityPatterns. Geografiska Annaler B, 2004, 86(4): 267-280.
[40] Shaw S L, Yu H B. A GIS-based time-geographic approach of studying individual activities and interactions in ahybrid physical-virtual space. Journal of Transport Geography, 2009, 17(2): 141-149.