大城市通勤方式与职住失衡的相互关系

doi:10.18306/dlkxjz.2018.09.010

Abstract

Abstract:

The spatial span of residents' job-housing places is increasingly expanded with urban sprawl. The diversity of commuting modes and improvement of commuting efficiency will affect commuters' previous job-housing imbalance. Commuting time as cost of commuting will contribute to better analyzing job-housing condition from commuters' perspective. Based on the statistical method of Bayesian-tobit and individual survey data of 7 sub-districts in Beijing, this study examined the interaction between four kinds of commuting modes (slow traffic, automobile, urban rail transit, and bus) and job-housing imbalance. Meanwhile, this study set employment accessibility and land use mix as moderator variables and explored their impacts on the relationship between varies commuting modes and job-housing imbalance. The study findings indicate that: (1) There is a negative relationship between commuting mode of slow traffic and job-housing imbalance. (2) By contrast, there is a positive dependency among automobile, urban rail transit, and bus and job-housing imbalance. (3) Employment accessibility and land use mix would weaken the original relationship among slow traffic, urban rail transit, and bus and job-housing imbalance. Specifically, under relatively low employment accessibility and land use mix conditions, the job-housing imbalance degree of slow traffic commuters is lower, while the job-housing imbalance degree of urban rail transit and bus commuters is higher. (4) Nevertheless, the relationship between commuting modes and job-housing imbalance will not be affected by employment accessibility and land use mix. These results suggest that (1) worse condition of employment accessibility and land use mixt can alleviate jobs-housing imbalance of commuters who use slow traffic. (2) However, lower value of employment accessibility and land use mix degree will aggravate job-housing imbalance of commuters. (3) In addition, the commuting behavior of automobile users will not be easily influenced by external factors.

Key words: commuting modes, job-housing imbalance, employment accessibility, land use mix, Bayesian-tobit, Beijing

Lifan SHEN, Chun ZHANG, He LI, Ye WANG. Interaction between commuting modes and job-housing imbalance in metropolis: An empirical study by Bayesian-tobit analysis in Beijing[J].PROGRESS IN GEOGRAPHY, 2018, 37(9): 1277-1290.

Figures/Tables 11

Fig.1

Tab.1

Fig. 2

Fig.3

Fig.4

Tab.2

Threshold test for employment accessibility and land use mix degree"

	AIC-滞后项	最佳滞后项参数	Tsay阈值检测	模型阈值	最小剩余平方和
就业可达性
总样本	AIC-lag = 8	d^*=2	$F 9,516 = 2212.06$ (0.000)	$γ = 0.817$	$RSS = 0.00357$
用地混合度
总样本	AIC-lag = 8	d^*=2	$F 9,516 = 335.22$ (0.000)	$γ = 1269.9$	$RSS = 0.00138$

Tab.2

Tab.3

Tab.4

Tab.5

Tab.6

Tab.7

References 62

[12]	龙瀛, 张宇, 崔承印. 2012. 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, 67(10): 1339-1352.
	[Long Y, Zhang Y, Cui C Y.2012. Identifying commuting pattern of Beijing using bus smart card data[J]. Acta Geographica Sinica, 67(10): 1339-1352.]
[13]	孟晓晨, 吴静, 沈凡卜. 2009. 职住平衡的研究回顾及观点综述[J]. 城市发展研究, 16(6): 23-35.
	[Meng X C, Wu J, Shen F B.2009. The studies review of urban jobs-housing balance[J]. Urban Studies, 16(6): 23-35.]
[14]	王淑伟. 2015. 站点周边用地特性对轨道客流影响机理研究[D]. 北京: 北京工业大学.
	[Wang S W.2015. Research on the influence mechanism of transit station surrounding land use on transit ridership[D]. Beijing, China: Beijing University of Technology.]
[15]	徐涛, 宋金平, 方琳娜, 等. 2009. 北京居住与就业的空间错位研究[J]. 地理科学, 29(2): 174-180.
	[Xu T, Song J P, Fang L N, et al.2009. Spatial mismatch between housing and employment in Beijing[J]. Scientia Geographica Sinica, 29(2): 174-180.]
[16]	张纯, 易成栋, 宋彦. 2016. 北京市职住空间关系特征及变化研究: 基于第五、六次人口普查和2001、2008年经济普查数据的实证分析[J]. 城市规划, 40(10): 59-64.
	[Zhang C, Yi C D, Song Y.2016. Characteristics of job-housing spatial relationship and changes in Beijing: An empirical study based on data from the 5th, 6th population census and economy census in 2001 and 2008[J]. City Planning Review, 40(10): 59-64.]
[17]	赵晖, 杨军, 刘常平. 2011. 轨道沿线居民职住分布及通勤空间组织特征研究: 以北京为例[J]. 经济地理, 31(9): 1445-1451.
	[Zhao H, Yang J, Liu C P.2011. Research on characteristics of the job/housing distribution and the changing commuting along subway lines: A case Study from Beijing[J]. Economic Geography, 31(9): 1445-1451.]
[18]	郑承志, 张旺锋, 武炳炎, 等. 2017. 北京市外来人口集聚型城中村流动人口职住分离研究[J]. 地理科学进展, 36(4): 416-425.
	[Zheng C Z, Zhang W F, Wu B Y.2011. Job-housing mismatch of floating population in urban villages of Beijing[J]. Progress in Geography, 36(4): 416-425.]
[19]	Cervero R, Day J.2010. 中国城市的郊区化与公交导向开发[J]. 上海城市规划, 93(4): 50-59.
	[Cervero R, Day J.2010. Suburbanization and transit-oriented development in China[J]. Shanghai Urban Planning Review, 93(4): 50-59.]
[20]	Alexander E.2005. Institutional transformation and planning: From institutionalization theory to institutional design[J]. Planning Theory, 4(3): 209-223. doi: 10.1177/1473095205058494
[21]	Barr S, Fraszczyk A, Mulley C.2010. Excess travelling-what does it mean? New definition and a case study of excess commuters in Tyne and Wear, UK[J]. European Transport Research Review, 2(2): 69-83. doi: 10.1007/s12544-010-0029-y
[22]	Bertaud D.2002. Note on transportation and urban spatial structure[C]//The Annual Bank Conference on Development and Economics. Washington, DC: The World Bank.
[23]	Boarnet M, Crane R.2001. The influence of land use on travel behavior: Specification and estimation strategies[J]. Transportation Research Part A: Policy & Practice, 35(9): 823-845.
[24]	Breusch T.1978. Testing for autocorrelation in dynamic linear models[J]. Australian Economic Papers, 17: 334-355. doi: 10.1111/j.1467-8454.1978.tb00635.x
[25]	Cervero R.1988. America's suburban centers: A study of the land use-transportation link[R]. DOT-T-88-14. Berkeley, CA: The University of California Transportation Center.
[26]	Cervero R.1989. Jobs-housing balancing and regional mobility[J]. Journal of the American Planning Association, 55(2): 136-150. doi: 10.1080/01944368908976014
[27]	Cervero R, Duncan M.2006. Which reduces vehicle travel more: Jobs-housing balance or retail-housing mixing[J]. Journal of the American Planning Association, 72(4): 475-490. doi: 10.1080/01944360608976767
[28]	Cervero R, Radisch C.1996. Travel choices in pedestrian versus automobile oriented neighborhoods[J]. Transport Policy, 3(3): 127-141. doi: 10.1016/0967-070X(96)00016-9
[29]	Chan K.1993. Consistency and limiting distribution of the least squares estimator of a threshold autoregressive model[J]. The Annals of Statistics, 21(1): 520-533. doi: 10.1214/aos/1176349040
[30]	Charron M.2007. From excess commuting to commuting possibilities: More extension to the concept of excess commuting[J]. Environment and Planning A: Economy & Space, 39(5): 1238-1254.
[31]	Ewing R, Cervero R.2010. Travel and the built environment: A meta-analysis[J]. Journal of the American Planning Association, 76(3): 265-294. doi: 10.1080/01944361003766766
[32]	Frank L, Pivo G.1994. Impacts of mixed use and density on utilization of three modes of travel: Single-occupant vehicle, transit, and walking[J]. Transportation Research Record, 1466: 44-52.
[33]	Fujii S, Garling T, Kitamura R.2001. Change in drivers' perception and use of public transport during a freeway closure: Effects of temporary structural change on cooperation in a real-life social dilemma[J]. Environment and Behavior, 33(6): 796-808. doi: 10.1177/00139160121973241
[34]	Giuliano G.1991. Is jobs housing balance a transportation issue[J]. Transportation Research Record, 1305: 305-312.
[35]	Godfrey L.1978. Testing for higher order serial correlation in regression equations when the regressors include lagged dependent variables[J]. Econometrica, 46(6):1303-1310. doi: 10.2307/1913830
[36]	Goodwin P.1976. Travel choice and time budgets[C]//Transportation Models, Summer Annual Meeting. Warwick, UK: University of Warwick.
[37]	Hamilton B, Roell A.1982. Wasteful commuting[J]. The Journal of Political Economy, 90(5): 1035-1053. doi: 10.1086/261107
[38]	Hansen B.2000. Sample splitting and threshold estimation[J]. Econometrica, 68(3): 575-603. doi: 10.1111/ecta.2000.68.issue-3
[39]	Hanson S, Schwab B.1987. Accessibility and intraurban travel[J]. Environment and Planning A: Economy & Space, 19(6): 735-748.
[40]	Horner M.2002. Extensions to the concept of excess commuting[J]. Environment and Planning A: Economy & Space, 34(3): 543-566.
[41]	Horner M, Murray E.2002. Excess commuting and the modifiable areal unit problem[J]. Urban Studies, 39(1): 131-139. doi: 10.1080/00420980220099113
[42]	Levinson D.1998. Accessibility and the journey to work[J]. Journal of Transport Geography, 6(1): 11-21. doi: 10.1016/S0966-6923(97)00036-7
[43]	Liu Z W, Deng W, Ji Y J, et al.2014. Influence of land use characteristics and trip attributes on commuting mode choice: A case of Nanjing[J]. Journal of Southeast University: English Edition, 30(1): 107-112.
[44]	Ma K, Banister D.2006. Extended excess commuting: A measure of the jobs-housing imbalance in Seoul[J]. Urban Studies, 43(11): 2099-2113. doi: 10.1080/00420980600945245
[45]	Mitchell R, Rapkin C.1954.Urban Traffic: A function of land use[M]. New York: Columbia University Press.
[46]	Niedzielski M, Horner M, Xiao N.2013. Analyzing scale independence in jobs-housing and commute efficiency metrics[J]. Transportation Research Part A: Policy & Practice, 58(3): 129-143.
[47]	Peng Z.1997. The jobs-housing balance and urban commuting[J]. Urban Studies, 34(8): 1215-1235. doi: 10.1080/0042098975600
[48]	Qin P, Wang L.2017. Job opportunities, institutions, and the jobs-housing spatial relationship: Case study of Beijing[J]. Transport Policy.(in press)
[49]	Saadi I, Boussauw K, Teller J, et al.2016. Trends in regional jobs-housing proximity based on the minimum commute: The case of Belgium[J]. Journal of Transport Geography, 57: 171-183. doi: 10.1016/j.jtrangeo.2016.10.010
[50]	Schafer A.2000. Regularities in travel demand: An international perspective. Journal of Transportation and Statistics, 3(3): 1-31.
[51]	Schleith D, Widener M, Kim C.2016. An examination of the jobs-housing balance of different categories of workers across 26 metropolitan regions[J]. Journal of Transport Geography, 57: 145-160. doi: 10.1016/j.jtrangeo.2016.10.008
[1]	柴彦威, 刘志林, 沈洁. 2008. 中国城市单位制度的变化及其影响[J]. 干旱区地理, 31(2): 155-163.
	[Chai Y W, Liu Z L, Shen J.2008. Changes of the Danwei system and its effects[J]. Arid Land Geography, 31(2): 155-163.]
[52]	Small K, Song S.1992. "Wasteful" commuting: A resolution[J]. Journal of Political Economy, 100(4): 888-898. doi: 10.1086/261844
[53]	Sung H, Choi K, Lee S, et al.2014. Exploring the impacts of land use by service coverage and station-level accessibility on rail transit ridership[J]. Journal of Transport Geography, 36(2): 134-140. doi: 10.1016/j.jtrangeo.2014.03.013
[2]	陈蕾, 孟晓晨. 2011. 北京市居住—就业空间结构及影响因素分析[J]. 地理科学进展, 30(10): 1210-1217. doi: 10.11820/dlkxjz.2011.10.002
	[Chen L, Meng X C.2011. A study on the job-housing spatial balance of Beijing[J]. Progress in Geography, 30(10): 1210-1217.] doi: 10.11820/dlkxjz.2011.10.002
[54]	Tanner J.1961. Factors affecting the amount of travel[R]. Road Research Technical Paper No. 5. London, UK: Her Majesty's Stationery Office.
[55]	Tsay R.1989. Testing and modeling threshold autoregressive processes[J]. Journal of the American Statistical Association, 84(405): 231-240. doi: 10.1080/01621459.1989.10478760
[56]	Wang D, Chai Y.2009. The jobs-housing relationship and commuting in Beijing, China: The legacy of Danwei[J]. Journal of Transport Geography, 17(1): 30-38. doi: 10.1016/j.jtrangeo.2008.04.005
[57]	Wang E, Song J, Xu T.2011. From "spatial bond" to "spatial mismatch": An assessment of changing jobs-housing relationship in Beijing[J]. Habitat International, 35(2): 398-409. doi: 10.1016/j.habitatint.2010.11.008
[3]	巢耀明. 2015. 南京新城区居住就业空间及协调发展机制研究[D]. 南京: 东南大学.
	[Chao Y M.2015. Research on residential-employment space and coordinated development mechanism of New Town in Nanjing[D]. Nanjing, China: Southeast University.]
[58]	White M.1988. Urban commuting journeys are not "wasteful"[J]. The Journal of Political Economy, 96(5): 1097-1110. doi: 10.1086/261579
[59]	Wu W, Hong J.2017. Does public transit improvement affect commuting behavior in Beijing, China? A spatial multilevel approach[J]. Transportation Research Part D: Transport & Environment, 52: 471-479.
[60]	Yan W, Wang D, Zhang S, et al.2018. Evaluating the multi-scale patterns of jobs-residence balance and commuting time-cost using cellular signaling data: A case study in Shanghai[J]. Transportation, 45: 1-16. doi: 10.1007/s11116-016-9716-4
[61]	Zahavi Y.1981. The UMOT-urban interaction[R]. DOT-RSPA-DPB. Washington, DC: U.S. Department of Transportation.
[4]	党云晓, 董冠鹏, 余建辉, 等. 2015. 北京土地利用混合度对居民职住分离的影响[J]. 地理学报, 70(6): 919-930.
	[Dang Y X, Dong G P, Yu J H, et al.2015. Impact of land-use mixed degree on resident's home-work separation in Beijing[J]. Acta Geographica Sinica, 70(6): 919-930.]
[5]	符婷婷, 张艳, 柴彦威. 2018. 大城市郊区居民通勤模式对健康的影响研究: 以北京天通苑为例[J]. 地理科学进展, 37(4): 547-555.
	[Fu T T, Zhang Y, Chai Y W.2018. Implications of commuting pattern for suburban residents'health in large Chinese cities: Evidences from Tiantongyuan in Beijing[J]. Progress in Geography, 37(4): 547-555.]
[6]	孔令斌. 2013. 城市平衡的影响因素及改善对策[J]. 城市交通, 11(6): 1-4.
	[Kong L B.2013. Influential factors and improvement measures for job-housing balance[J]. Urban Transport of China, 11(6): 1-4.]
[7]	李俊芳, 姚敏锋, 季峰, 等. 2016. 土地利用混合度对轨道交通车站客流的影响[J]. 同济大学学报: 自然科学版, 44(9): 1415-1423.
	[Li J F, Yao M F, Ji F, et al.2016. Quantitative study on how land use mix impact urban rail transit at station-level[J]. Journal of Tongji University: Natural Science, 44(9): 1415-1423.]
[8]	林红, 李军. 2008. 出行空间分布与土地利用混合程度关系研究: 以广州中心片区为例[J]. 城市规划, 32(9): 53-56, 74.
	[Lin H, Li J.2008. Relationship between spatial distribution of resident trips and mixed degree of land use: A case study of Guangzhou[J]. City Planning Review, 32(9): 53-56, 74.]
[9]	刘望保, 闫小培, 谢丽娟. 2012. 转型时期广州居民职住流动及其空间结构变化: 基于3个年份的调查分析[J]. 地理研究, 31(9): 1685-1696.
	[Liu W B, Yan X P, Xie L J.2012. Employment and residential mobility and its spatial structure change based on the 3 years' survey analysis[J]. Geographical Research, 31(9): 1685-1696.]
[10]	刘志林, 王茂军. 2011. 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[J]. 地理学报, 66(4): 457-467.
	[Liu Z L, Wang M J.2011. Job accessibility and its impacts on commuting time of urban residents in Beijing: From a spatial mismatch perspective[J]. Acta Geographica Sinica, 66(4): 457-467.]
[11]	刘志林, 张艳, 柴彦威. 2009. 中国大城市职住分离现象及其特征: 以北京市为例[J]. 城市发展研究, 16(9): 123-130.
	[Liu Z L, Zhang Y, Chai Y W.2009. Home-work separation in the context of institutional and spatial transformation in urban China: Evidence from Beijing household survey data[J]. Urban Studies, 16(9): 123-130.]
[62]	Zhao P, Lv B, Roo G.2011. Impact of the jobs-housing balance on urban commuting in Beijing in the transformation era[J]. Journey of Transport Geography, 19(1): 59-69. doi: 10.1016/j.jtrangeo.2009.09.008

变量	定义和表达方式	数据来源
过剩通勤	表示职住失衡的程度。用平均实际通勤时间(min)和理论最小通勤时间(min)之差与平均实际通勤时间(min)的比值表示	调查问卷
慢行交通	是否主要使用慢行交通的通勤方式,包括步行和自行车	调查问卷
机动车	是否主要使用机动车的通勤方式,包括私家车、出租车、公车、摩托车	调查问卷
轨道交通	是否主要使用轨道交通的通勤方式,包括地铁和轻轨	调查问卷
地面公交	是否主要使用公交车的通勤方式,包括公交(电)车、校车、班车	调查问卷
就业可达性	街道的就业可达性。用加权后的街道就业岗位密度(个/ km²)表示,据式(6)计算得出	调查问卷,第二次全国经济普查,第六次全国人口普查
用地混合度	街道的用地混合度。用下文式(7)计算得出	谷歌地图,实地调查
年龄	具体值(岁)	调查问卷
性别	男/女	调查问卷
户口状况	有/无本地户口	调查问卷
教育状况	研究生及以上/本科或大专/高中或中专/初中/小学/小学以下	调查问卷
家庭规模	家庭人口数(人)	调查问卷
家庭收入	<2000元;2001~5000元;5001~10000元;10001~20000元;>20000	调查问卷
住房状况	自有房/租住房	调查问卷
人口密度	街道的人口密度,用街道常住人口数(人)与街道面积(km²)的比值表示	第六次全国人口普查
公服设施密度	街道的商业和公共设施密度,用街道内商业和公共设施(综合商场、医院、卫生服务站、中小学校)数(个)与街道面积(km²)的比值表示	高德地图
可达性	轨道站点的可达性,用受访者从居住地到最近轨道站点的步行时间(min)表示	调查问卷

变量	相关系数(标准误差)	95%的置信区间
慢行交通	-14.43 (1.71)	-17.77	-11.06
机动车	7.54 (1.74)	4.15	10.96
轨道交通	10.30 (1.61)	7.15	13.48
地面公交	6.67 (1.23)	4.28	9.10
年龄	0.11 (0.08)	-0.03	0.26
性别	1.82 (1.12)	-0.37	4.01
户口状况	0.94 (1.18)	-1.38	3.24
教育状况	-0.29 (0.59)	-1.46	0.87
家庭规模	-0.44 (0.45)	-1.33	0.44
收入状况	1.48 (0.61)	-0.29	2.69
住房状况	0.07 (1.38)	-2.64	2.75
可达性	0.33 (0.09)	0.16	0.51
人口密度	0.00 (0.00)	-0.00	0.01
公服设施密度	-0.83 (1.36)	-3.49	1.80

变量	相关系数(标准误差)	95%的置信区间
慢行交通	-7.14 (16.80)	-40.46	25.81
机动车	0.03 (10.94)	-21.29	21.61
轨道交通	7.68 (6.78)	-5.60	21.05
地面公交	-0.28 (11.73)	-23.32	22.82
年龄	0.14 (0.09)	-0.04	0.32
性别	1.47 (1.35)	-1.19	4.12
户口状况	-0.63 (1.43)	-3.40	2.18
教育状况	-1.16 (0.71)	-2.56	0.23
家庭规模	-0.75 (0.55)	-1.84	0.33
收入状况	2.89 (0.73)	1.45	4.31
住房状况	1.03 (1.68)	-2.26	4.33
可达性	0.25 (0.11)	0.04	0.47
人口密度	0.00 (0.00)	-0.00	0.00
公服设施密度	-2.89 (1.66)	-6.12	0.34

变量	相关系数(标准误差)	95%的置信区间
慢行交通	-16.46 (1.64)	-19.65	-13.26
机动车	1.72 (1.32)	-0.86	4.30
轨道交通	4.90 (1.30)	5.19	10.30
地面公交	7.74 (1.23)	2.50	7.30
年龄	0.08 (0.07)	-0.06	0.23
性别	2.12 (1.11)	-0.06	4.29
户口状况	1.47 (1.19)	-0.82	3.82
教育状况	-0.18 (0.60)	-1.35	0.99
家庭规模	-0.49 (0.45)	-1.38	0.40
收入状况	1.89 (0.62)	0.70	3.09
住房状况	0.37 (1.39)	-2.33	3.11
可达性	0.35 (0.09)	0.17	0.53
人口密度	0.00 (0.00)	-0.00	0.00
公服设施密度	0.21 (1.40)	-2.54	3.00

变量	相关系数(标准误差)	95%的置信区间
截距	76.05 (5.22)	65.82	86.19
慢行交通	-1.18 (17.81)	-36.24	33.40
机动车	6.07 (17.73)	-28.77	41.27
轨道交通	-0.18 (9.64)	-19.15	18.88
地面公交	-0.29 (18.78)	-37.05	36.38
年龄	0.14 (0.09)	-0.04	0.31
性别	1.35 (1.35)	-1.31	4.00
户口状况	-0.42 (1.43)	-3.24	2.38
教育状况	-1.16 (0.71)	-2.56	0.23
家庭规模	-0.75 (0.55)	-1.84	0.33
收入状况	2.98 (0.73)	1.53	4.43
住房状况	1.08 (1.68)	-2.72	4.41
可达性	0.25 (0.11)	0.03	0.46
人口密度	0.00 (0.00)	-0.00	0.00
公服设施密度	-3.23 (1.69)	-6.53	0.10

Interaction between commuting modes and job-housing imbalance in metropolis: An empirical study by Bayesian-tobit analysis in Beijing

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变量	相关系数(标准误差)	95%的置信区间
截距	66.76 (4.77)	57.45	76.04
慢行交通	-16.05 (1.64)	-19.26	-12.82
机动车	2.05 (1.32)	-50.55	4.64
轨道交通	8.11 (1.29)	5.56	10.60
地面公交	5.09 (1.21)	2.73	7.47
年龄	0.09 (0.07)	-0.08	0.23
性别	2.21 (1.12)	0.02	4.40
户口状况	1.28 (1.17)	-1.03	3.57
教育状况	-0.14 (0.60)	-1.32	1.02
家庭规模	-0.51 (0.46)	-1.40	0.39
收入状况	1.83 (0.61)	0.63	3.03
住房状况	0.33 (1.38)	-2.37	3.02
可达性	0.36 (0.09)	0.18	0.54
人口密度	-0.00 (0.00)	-0.00	0.00
公服设施密度	0.28 (1.39)	-2.46	3.03

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