Abstract
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.
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References
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
Alonso W, 1964. Location and Land Use. USA: Harvard University Press.
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
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
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
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
Cervero R, 1989. Jobs-housing balancing and regional mobility. Journal of the American Planning Association, 55(2): 136–150. doi: 10.1080/01944368908976014
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)
Crane R, 1999. The Impacts of Urban Form on Travel: A Critical Review. Lincoln Institute of Land Policy.
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
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
Dodd S C, 1950. The inheritance hypothesis—a gravity model fitting physical masses and human groups. American Sociological Review, 15: 245–256.
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
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
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
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
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
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
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
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
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
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
Kain J F, 1968. Housing segregation, Negro employment, and metropolitan decentralization. The Quarterly Journal of Economic, 82(2): 175–197.
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
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
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)
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)
Liu Wangbao, Hou Changying, 2014. Urban residents' homework space and commuting behavior in Guangzhou City. Scientia Geographica Sinica, 69(3): 272–279. (in Chinese)
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)
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)
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
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)
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
Niedzielski M A, 2006. A spatially disaggregated approach to commuting efficiency. Urban Studies, 43(13): 2485–2502. doi: 10.1080/00420980600970672
Peng Z R, 1997. The jobs-housing balance and urban commuting. Urban Studies, 34(8): 1215–1235. doi: 10.1080/0042098975600
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
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
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
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
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
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)
Sun Tieshan, 2015. Spatial mismatch between residences and jobs by sectors in Beijing and its explanations. Geographical Research, 34(2): 351–363. (in Chinese)
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.
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
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
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
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
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)
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
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
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)
Zhou Suhong, Yan Xiaopei, 2005. Characteristics of jobs-housing and organization in Guangzhou City. Scientia Geographica Sinica, 60(6): 6664–6670. (in Chinese)
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
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Foundation item: Under the auspices of National Natural Science Foundation of China (No. 41271182)
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Dai, D., Zhou, C. & Ye, C. Spatial-temporal characteristics and factors influencing commuting activities of middle-class residents in Guangzhou City, China. Chin. Geogr. Sci. 26, 410–428 (2016). https://doi.org/10.1007/s11769-016-0806-1
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DOI: https://doi.org/10.1007/s11769-016-0806-1