留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Spatial-temporal Characteristics and Factors Influencing Commuting Activities of Middle-class Residents in Guangzhou City, China

DAI Dandan ZHOU Chunshan YE Changdong

DAI Dandan, ZHOU Chunshan, YE Changdong. Spatial-temporal Characteristics and Factors Influencing Commuting Activities of Middle-class Residents in Guangzhou City, China[J]. 中国地理科学, 2016, 26(3): 410-428. doi: 10.1007/s11769-016-0806-1
引用本文: DAI Dandan, ZHOU Chunshan, YE Changdong. Spatial-temporal Characteristics and Factors Influencing Commuting Activities of Middle-class Residents in Guangzhou City, China[J]. 中国地理科学, 2016, 26(3): 410-428. doi: 10.1007/s11769-016-0806-1
DAI Dandan, ZHOU Chunshan, YE Changdong. Spatial-temporal Characteristics and Factors Influencing Commuting Activities of Middle-class Residents in Guangzhou City, China[J]. Chinese Geographical Science, 2016, 26(3): 410-428. doi: 10.1007/s11769-016-0806-1
Citation: DAI Dandan, ZHOU Chunshan, YE Changdong. Spatial-temporal Characteristics and Factors Influencing Commuting Activities of Middle-class Residents in Guangzhou City, China[J]. Chinese Geographical Science, 2016, 26(3): 410-428. doi: 10.1007/s11769-016-0806-1

Spatial-temporal Characteristics and Factors Influencing Commuting Activities of Middle-class Residents in Guangzhou City, China

doi: 10.1007/s11769-016-0806-1
基金项目: Under the auspices of National Natural Science Foundation of China (No. 41271182)
详细信息
    通讯作者:

    ZHOU Chunshan

Spatial-temporal Characteristics and Factors Influencing Commuting Activities of Middle-class Residents in Guangzhou City, China

Funds: Under the auspices of National Natural Science Foundation of China (No. 41271182)
More Information
    Corresponding author: ZHOU Chunshan
  • 摘要: The middle class in metropolitan Chinese cities has become an important social group. With the rapid development of urbanization and constant advancement of suburbanization, the middle class has increasingly come to influence city traffic. Research into middle-class commuting activities thus has practical significance for improving traffic congestion and reducing the commuting burden in metropolitan cities. Based on a dataset formed by 816 completed surveys, this paper analyzes the commuting mode, time and distance of middle-class residents in Guangzhou City using the descriptive statistical method. The results indicate that private cars are the main commuting mode, followed by public transport. Meanwhile, middle-class residents mainly undertake medium-short time and medium-short distance commuting. The study subsequently uses multilevel logistic regression and multiple linear regression models to analyze the factors that influence commuting mode choice, time and distance. The gender, age, number of family cars, housing source and jobs-housing balance are the most important factors influencing commuting mode choice; housing, population density, jobs-housing balance and commuting mode significantly affect commuting time; and transport accessibility, jobs-housing balance and commuting mode are the notable factors affecting commuting distance. Finally, this paper analyzes what is affecting the commuting activities of middle-class residents and determines the differences in commuting activity characteristics and influence factors between middle-class and ordinary residents. Policy suggestions to improve urban planning and urban management are also proposed.
  • [1] Abraham J E, Hunt J D, 1997. Specification and estimation of nested logit model of home, workplaces, and commuter mode choices by multiple-worker households. Journal of the Transportation Research Board, 1606(1): 17-24. doi:  10.3141/1606-03
    [2] Alonso W, 1964. Location and Land Use. USA: Harvard University Press.
    [3] Anas A, 1981. The estimation of multinomial logit models of joint location and travel mode choice from aggregated data. Journal of Regional Science, 21(2): 223-242. doi: 10.1111/j. 1467-9787.1981.tb00696.x
    [4] Black W R, 1997. North American transportation: perspectives on research needs and sustainable transportation. Journal of Transport Geography, 5(1): 12-19. doi: 10.1016/S0966-6923 (96)00042-7
    [5] Blumenberg E, 2004. En-gendering effective planning: spatial mismatch, low-income women, and transportation policy. Journal of the American Planning Association, 70(3): 269-281. doi:  10.1080/01944360408976378
    [6] Blumenberg E, Manville M, 2004. Beyond the spatial mismatch: welfare recipients and transportation policy. Journal of Planning Literature, 19(2): 182-205. doi: 10.1177/0885412204 269103
    [7] Cervero R, 1989. Jobs-housing balancing and regional mobility. Journal of the American Planning Association, 55(2): 136-150. doi:  10.1080/01944368908976014
    [8] Chai Yanwei, Zhang Yan, Liu Zhilin, 2011. Spatial differences of home-work separation and the impacts of housing policy and urban sprawl: evidence from household survey data in Beijing. Acta Geographica Sinica, 66(2): 157-166. (in Chinese)
    [9] Crane R, 1999. The Impacts of Urban Form on Travel: A Critical Review. Lincoln Institute of Land Policy.
    [10] Cropper M L, Gordon P L, 1991. Wasteful commuting: a re-examination. Journal of Urban Economics, 29(1): 2-13. doi:  10.1016/0094-1190(91)90022-Y
    [11] Danyluk M, Ley D, 2007. Modalities of the new middle class: ideology and behaviour in the journey to work from gentrified neighbourhoods in Canada. Urban Studies, 44(11): 2195-2210. doi:  10.1080/00420980701520277
    [12] Dodd S C, 1950. The inheritance hypothesis—a gravity model fitting physical masses and human groups. American Sociological Review, 15: 245-256.
    [13] Elldér E, 2014. Commuting choices and residential built environments in Sweden, 1990-2010: a multilevel analysis. Urban Geography, 35(5): 715-734. doi: 10.1080/02723638.2014. 916906
    [14] Fan Z J, Foley M P, Rauser E et al., 2013. Effects of residential location and work-commuting on long-term work disability. Journal of Occupational Rehabilitation, 23(4): 610-620. doi:  10.1007/s10926-013-9424-2
    [15] Festini F, Ciofi D, Bisogni S, 2011. Commuting patterns among Italian nurses: a cross-sectional study. International Nursing Review, 58(3): 354-360. doi: 10.1111/j.1466-7657.2011. 00881.x
    [16] Giuliano G, Small K A, 1993. Is the journey to work explained by urban structure? Urban Studies, 30(9): 1485-1500. doi: 10. 1080/00420989320081461
    [17] Gordon P, Kumar A, Richardson H W, 1989. Congestion, changing metropolitan structure, and city size in the United States. International Regional Science Review, 12(1): 45-56. doi:  10.1177/016001768901200103
    [18] Guest A M, Cluett C, 1976. Workplace and residential location: a push-pull model. Journal of Regional Science, 16(3): 399-410. doi:  10.1111/j.1467-9787.1976.tb00984.x
    [19] Hanson S, 1982. The determinants of daily travel-activity patterns: relative location and sociodemographic factors. Urban Geography, 3(3): 179-202. doi:  10.2747/0272-3638.3.3.179
    [20] Hansson E, Mattisson K, Björk J et al., 2011. Relationship between commuting and health outcomes in a cross-sectional population survey in southern Sweden. BMC Public Health, 11(1): 834. doi:  10.1186/1471-2458-11-834
    [21] Hong J, Shen Q, Zhang L, 2014. How do built-environment factors affect travel behavior? A spatial analysis at different geographic scales. Transportation, 41(3): 419-440. doi:  10.1007/s11116-013-9462-9
    [22] Horner M W, 2004. Spatial dimensions of urban commuting: a review of major issues and their implications for future geographic research. The Professional Geographer, 56(2): 160-173. doi:  10.1111/j.0033-0124.2004.05602002.x
    [23] Kain J F, 1968. Housing segregation, Negro employment, and metropolitan decentralization. The Quarterly Journal of Economic, 82(2): 175-197.
    [24] Kawabata M, Shen Q, 2007. Commuting inequality between cars and public transit: the case of the San Francisco Bay Area, 1990-2000. Urban Studies, 44(9): 1759-1780. doi:  10.1080/00420980701426616
    [25] Levinson D M, Kumar A, 1997. Density and the journey to work. Growth and Change, 28(2): 147-172. doi:  10.1111/j.1468-2257.1997.tb00768.x
    [26] Liu Baokui, Feng Changchun, 2012. Commuting pattern and spatial relation between residence and employment of migrant workers in metropolitan areas: the case of Beijing. Urban Planning Forum, 4: 59-64. (in Chinese)
    [27] Liu Dinghui, Zhu Chaohong, Yang Yongchun, 2014. The characteristics of resident commuting and its relationship with urban spatial structure in large cities of Western China: a case study of Chengdu. Human Geography, 29(2): 61-68. (in Chinese)
    [28] Liu Wangbao, Hou Changying, 2014. Urban residents' home-work space and commuting behavior in Guangzhou City. Scientia Geographica Sinica, 69(3): 272-279. (in Chinese)
    [29] Liu Zhilin, Wang Maojun, 2011. Job accessibility and its impacts on commuting time of urban residents in Beijing: from a spatial mismatch perspective. Acta Geographica Sinica, 66(4): 457-467. (in Chinese)
    [30] Lu Xueyi, 2010. Social strata structure change in contemporary China since 1949. Journal of Beijing University of Technology (Social Sciences Edition), 3: 1-12. (in Chinese)
    [31] Maat K, Van Wee B, Stead D, 2005. Land use and travel behaviour: expected effects from the perspective of utility theory and activity-based theories. Environment and Planning B: Planning and Design, 32: 33-46. doi:  10.1068/b31106
    [32] Meng Bin, Zheng Limin, Yu Huili, 2011. Commuting time change and its influencing factors in Beijing. Progress in Geography, 23(10): 1218-1224. (in Chinese)
    [33] Muth R F, 1961. The spatial structure of the housing market. Papers in Regional Science, 7(1): 207-220. doi:  10.1111/j.1435-5597.1961.tb01780.x
    [34] Niedzielski M A, 2006. A spatially disaggregated approach to commuting efficiency. Urban Studies, 43(13): 2485-2502. doi:  10.1080/00420980600970672
    [35] Peng Z R, 1997. The jobs-housing balance and urban commuting. Urban Studies, 34(8): 1215-1235. doi:  10.1080/0042098975600
    [36] Sanchez T W, Shen Q, Peng Z R, 2004. Transit mobility, jobs access and low-income labour participation in US metropolitan areas. Urban Studies, 41(7): 1313-1331. doi: 10.1080/00 42098042000214815
    [37] Schwanen T, Dieleman F M, Dijst M, 2004. The impact of metropolitan structure on commute behavior in the Netherlands: a multilevel approach. Growth and Change, 35(3): 304-333. doi:  10.1111/j.1468-2257.2004.00251.x
    [38] Silva S G, Del Duca G F, Silva K S et al., 2012. Commuting to and from work and factors associated among industrial workers from Southern Brazil. Revista de Saúde Pública, 46(1): 180-184. doi:  10.1590/S0034-89102011005000084
    [39] Stead D, 2001. Relationships between land use, socioeconomic factors, and travel patterns in Britain. Environment and Planning B, 28(4): 499-528. doi:  10.1068/b2677
    [40] Sultana S, 2002. Job/housing imbalance and commuting time in the Atlanta metropolitan area: exploration of causes of longer commuting time. Urban Geography, 23(8): 728-749. doi:  10.2747/0272-3638.23.8.728
    [41] Sun Bindong, Pan Xin, 2008. The impact research on daily travel by urban spatial structure: from the points of view of mono-centric and poly-centric. Urban Problems, 1: 47-50. (in Chinese)
    [42] Sun Tieshan, 2015. Spatial mismatch between residences and jobs by sectors in Beijing and its explanations. Geographical Research, 34(2): 351-363. (in Chinese)
    [43] Tarumi K, 1992. An inquiry into the effects of working time and commuting time on lifestyle in white-collar workers. Nippon Koshu Eisei Zasshi, 39(3): 163-171.
    [44] Vandersmissen M H, Villeneuve P, Thériault M, 2003. Analyzing changes in urban form and commuting time. The Professional Geographer, 55(4): 446-463. doi:  10.1111/0033-0124.5504004
    [45] Wachs M, Taylor B D, Levine N et al., 1993. The changing commute: a case-study of the jobs-housing relationship over time. Urban Studies, 30(10): 1711-1729. doi: 10.1080/004209 89320081681
    [46] Wang F, 2000. Modeling commuting patterns in Chicago in a GIS environment: a job accessibility perspective. The Professional Geographer, 52(1): 120-133. doi:  10.1111/0033-0124.00210
    [47] Wang F, 2001. Explaining intraurban variations of commuting by job proximity and workers' characteristics. Environment and Planning B, 28(2): 169-182. doi:  10.1068/b2710
    [48] Wang Maojun, Song Guoqing, Xu Jie, 2009. Data mining on commuting distance mode of urban residents based on the analysis of decision tree. Geographical Research, 28(6): 1516-1527. (in Chinese)
    [49] Watts M J, 2009. The impact of spatial imbalance and socioeconomic characteristics on average distance commuted in the Sydney metropolitan area. Urban Studies, 46(2): 317-339. doi:  10.1177/0042098008099357
    [50] Zhao P, Lü B, De Roo G, 2011. Impact of the jobs-housing balance on urban commuting in Beijing in the transformation era. Journal of Transport Geography, 19(1): 59-69. doi: 10.1016/j. jtrangeo.2009.09.008
    [51] Zhou Jiangping, Chen Xiaojian, Huang Wei et al., 2013. Jobs-housing balance and commute efficiency in cities of Central and Western China: a case study of Xi'an. Acta Geogra­phica Sinica, 68(10): 1316-1330. (in Chinese)
    [52] Zhou Suhong, Yan Xiaopei, 2005. Characteristics of jobs-housing and organization in Guangzhou City. Scientia Geographica Sinica, 60(6): 6664-6670. (in Chinese)
    [53] Zhou Suhong, Deng Lifang, Huang Meiyu, 2013. Spatial analysis of commuting mode choice in Guangzhou City, China. Chinese Geographical Science, 23(3): 353-364. doi: 10.1007/s 11769-012-0569-2
  • [1] Hu YU, Xiaoyao ZHANG, Yu DENG.  Spatiotemporal Evolution and Influencing Factors of Landscape Ecological Vulnerability in the Three-River-Source National Park Region . Chinese Geographical Science, 2022, 32(5): 852-866. doi: 10.1007/s11769-022-1297-x
    [2] Haipeng ZHANG, Hanchu LIU, Yong SUN, Renwei HE.  Spatial Differentiation Characteristics of Human Settlements and Their Responses to Natural and Socioeconomic Conditions in the Marginal Zone of an Uninhabited Area, Changtang Plateau, China . Chinese Geographical Science, 2022, 32(3): 506-520. doi: 10.1007/s11769-022-1280-6
    [3] Ruiling HAN, Lingling LI, Xiaoyan ZHANG, Zi LU, Shaohua ZHU.  Spatial-temporal Evolution Characteristics and Decoupling Analysis of Influencing Factors of China’s Aviation Carbon Emissions . Chinese Geographical Science, 2022, 32(2): 218-236. doi: 10.1007/s11769-021-1247-z
    [4] Xiaohong CHEN, Mingxuan ZHANG, Ying WANG, Xiaoqing XU, Shuang LIU, Lingyu MA.  Coupling and Coordination Characteristics and Influencing Factors of the Livable Environment System for the Elderly in China . Chinese Geographical Science, 2022, (): 1-17. doi: 10.1007/s11769-022-1283-3
    [5] Zheng CAO, Ya WEN, Song SONG, Chak Ho HUNG, Hui SUN.  Spatiotemporal Variations and Controls on Anthropogenic Heat Fluxes in 12 Selected Cities in the Eastern China . Chinese Geographical Science, 2021, 31(3): 444-458. doi: 10.1007/s11769-021-1203-y
    [6] Jiawei WANG, Shilin YE, Xinhua QI.  Regional Equity and Influencing Factor of Social Assistance in China . Chinese Geographical Science, 2021, 31(4): 611-628. doi: 10.1007/s11769-021-1195-7
    [7] Xuelan TAN, Hangling YU, Yue AN, Zhenkai WANG, Lingxiao JIANG, Hui REN.  Spatial Differentiation and Influencing Factors of Poverty Alleviation Performance Under the Background of Sustainable Development: A Case Study of Contiguous Destitute Areas in Hunan Province, China . Chinese Geographical Science, 2021, 31(6): 1029-1044. doi: 10.1007/s11769-021-1242-4
    [8] Dalai MA, Fengtai ZHANG, Lei GAO, Guangming YANG, Qing YANG, Youzhi AN.  Spatiotemporal Dynamics of Green Total-factor Water-use Efficiency and Its Influencing Factors in China . Chinese Geographical Science, 2021, 31(5): 795-814. doi: 10.1007/s11769-021-1227-3
    [9] Xingchuan GAO, Tao LI, Dongqi SUN.  Regional Differentiation Regularity and Influencing Factors of Population Change in the Qinghai-Tibet Plateau, China . Chinese Geographical Science, 2021, 31(5): 888-899. doi: 10.1007/s11769-021-1223-7
    [10] Chen CHEN, Lin CHENG, Chunliang XIU, Jiuquan LI.  Spatial Mismatch or Not? Evidence from Public Janitors in Xi ’an, China . Chinese Geographical Science, 2021, 31(2): 376-386. doi: 10.1007/s11769-021-1194-8
    [11] SUN Wu, LI Tao.  Building Height Trends and Their Influencing Factors under China's Rapid Urbanization: A Case Study of Guangzhou, 1960-2017 . Chinese Geographical Science, 2020, 30(6): 993-1004. doi: 10.1007/s11769-020-1162-8
    [12] SUN Zhe, ZHAN Dongsheng, JIN Fengjun.  Spatio-temporal Characteristics and Geographical Determinants of Air Quality in Cities at the Prefecture Level and Above in China . Chinese Geographical Science, 2019, 20(2): 316-324. doi: 10.1007/s11769-019-1031-5
    [13] DIAO Shuo, YUAN Jiadong, WU Yanyan.  Performance Evaluation of Urban Comprehensive Carrying Capacity of Harbin, Heilongjiang Province in China . Chinese Geographical Science, 2019, 20(4): 579-590. doi: 10.1007/s11769-019-1056-9
    [14] XUE Shuyan, LI Gang, YANG Lan, LIU Ling, NIE Qifan, Muhammad Sajid MEHMOOD.  Spatial Pattern and Influencing Factor Analysis of Attended Collection and Delivery Points in Changsha City, China . Chinese Geographical Science, 2019, 29(6): 1078-1094. doi: 10.1007/s11769-019-1086-3
    [15] LI Bo, SHI Zhaoyuan, TIAN Chuang.  Spatio-temporal Difference and Influencing Factors of Environmental Adaptability Measurement of Human-sea Economic System in Liaoning Coastal Area . Chinese Geographical Science, 2018, 28(2): 313-324. doi: 10.1007/s11769-018-0948-4
    [16] ZHAO Fuqiang, QI Lin, FANG Lei, YANG Jian.  Influencing Factors of Seed Long-distance Dispersal on a Fragmented Forest Landscape on Changbai Mountains, China . Chinese Geographical Science, 2016, 26(1): 68-77. doi: 10.1007/s11769-015-0747-0
    [17] LIU Wangbao, HOU Quan.  Excess Commuting in Transitional Urban China: A Case Study of Guangzhou . Chinese Geographical Science, 2016, 26(5): 599-608. doi: 10.1007/s11769-015-0793-7
    [18] YU Chao, MA Yanji.  Carbon Emission Trends of Manufacturing and Influencing Factors in Jilin Province, China . Chinese Geographical Science, 2016, 26(5): 656-669. doi: 10.1007/s11769-016-0823-0
    [19] ZHOU Suhong, DENG Lifang, HUANG Meiyu.  Spatial Analysis of Commuting Mode Choice in Guangzhou, China . Chinese Geographical Science, 2013, 23(3): 353-364. doi: 10.1007/s11769-012-0569-2
    [20] CAO Xiaoshu, CHEN Hemei, LI Linna, ZHEN Feng.  Private Car Travel Characteristics and Influencing Factors in Chinese Cities——A Case Study of Guangzhou in Guangdong, China . Chinese Geographical Science, 2009, 19(4): 325-332. doi: 10.1007/s11769-009-0325-4
  • 加载中
计量
  • 文章访问数:  443
  • HTML全文浏览量:  18
  • PDF下载量:  1718
  • 被引次数: 0
出版历程
  • 收稿日期:  2015-06-02
  • 修回日期:  2015-09-28
  • 刊出日期:  2016-06-27

Spatial-temporal Characteristics and Factors Influencing Commuting Activities of Middle-class Residents in Guangzhou City, China

doi: 10.1007/s11769-016-0806-1
    基金项目:  Under the auspices of National Natural Science Foundation of China (No. 41271182)
    通讯作者: ZHOU Chunshan

摘要: The middle class in metropolitan Chinese cities has become an important social group. With the rapid development of urbanization and constant advancement of suburbanization, the middle class has increasingly come to influence city traffic. Research into middle-class commuting activities thus has practical significance for improving traffic congestion and reducing the commuting burden in metropolitan cities. Based on a dataset formed by 816 completed surveys, this paper analyzes the commuting mode, time and distance of middle-class residents in Guangzhou City using the descriptive statistical method. The results indicate that private cars are the main commuting mode, followed by public transport. Meanwhile, middle-class residents mainly undertake medium-short time and medium-short distance commuting. The study subsequently uses multilevel logistic regression and multiple linear regression models to analyze the factors that influence commuting mode choice, time and distance. The gender, age, number of family cars, housing source and jobs-housing balance are the most important factors influencing commuting mode choice; housing, population density, jobs-housing balance and commuting mode significantly affect commuting time; and transport accessibility, jobs-housing balance and commuting mode are the notable factors affecting commuting distance. Finally, this paper analyzes what is affecting the commuting activities of middle-class residents and determines the differences in commuting activity characteristics and influence factors between middle-class and ordinary residents. Policy suggestions to improve urban planning and urban management are also proposed.

English Abstract

DAI Dandan, ZHOU Chunshan, YE Changdong. Spatial-temporal Characteristics and Factors Influencing Commuting Activities of Middle-class Residents in Guangzhou City, China[J]. 中国地理科学, 2016, 26(3): 410-428. doi: 10.1007/s11769-016-0806-1
引用本文: DAI Dandan, ZHOU Chunshan, YE Changdong. Spatial-temporal Characteristics and Factors Influencing Commuting Activities of Middle-class Residents in Guangzhou City, China[J]. 中国地理科学, 2016, 26(3): 410-428. doi: 10.1007/s11769-016-0806-1
DAI Dandan, ZHOU Chunshan, YE Changdong. Spatial-temporal Characteristics and Factors Influencing Commuting Activities of Middle-class Residents in Guangzhou City, China[J]. Chinese Geographical Science, 2016, 26(3): 410-428. doi: 10.1007/s11769-016-0806-1
Citation: DAI Dandan, ZHOU Chunshan, YE Changdong. Spatial-temporal Characteristics and Factors Influencing Commuting Activities of Middle-class Residents in Guangzhou City, China[J]. Chinese Geographical Science, 2016, 26(3): 410-428. doi: 10.1007/s11769-016-0806-1
参考文献 (53)

目录

    /

    返回文章
    返回