Please wait a minute...
Journal of Zhejiang University (Science Edition)  2022, Vol. 49 Issue (3): 376-383    DOI: 10.3785/j.issn.1008-9497.2022.03.015
Urban Science     
The effects of "T" and "D" factors of TOD on reducing the car kilometers traveled
Yiling DENG()
School of Design and Architecture,Zhejiang University of Technology,Hangzhou 310023,China
Download: HTML( 2 )   PDF(3957KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

Most of the studies on transit-oriented development TOD in China were focused on planning practice, and to what extent TOD can reduce the urban residents' car kilometers traveled was unclear. Based on the Nanjing household travel survey data, the "T" factor of TOD was defined by whether there was a subway station within the distance of 800 m from the household residence; the "D" factor of TOD was defined by residence density, mix, and walk score. After controlling for socioeconomic and home location factors, a Tobit model was proposed to study the effect of TOD on reducing the urban residents' car kilometers traveled. By classifying four residence types (i.e., TOD, only-T, only-D, and non-TOD) and analyzing the marginal effects, we found that the car kilometers traveled decreases by 19.6% when the "T" factor changes from no subway station to one subway station; and it decreased by 0.7% for every 0.01 increase in the "D" factor; the car kilometers traveled decreased by 43.4% if the effect of both "T" and "D" were combined. Based on the above findings, it was suggested that the planning studies should shift from only-T to TOD and from non-TOD to only-D.



Key wordsTOD      car kilometers traveled      walk score      Tobit model      marginal effect     
Received: 18 March 2021      Published: 24 May 2022
CLC:  TU 984.191  
Cite this article:

Yiling DENG. The effects of "T" and "D" factors of TOD on reducing the car kilometers traveled. Journal of Zhejiang University (Science Edition), 2022, 49(3): 376-383.

URL:

https://www.zjujournals.com/sci/EN/Y2022/V49/I3/376


TOD中“T”与“D”因素对小汽车出行距离的减量影响

国内以公共交通为导向的开发(transit-oriented development,TOD)研究大多集中在规划实践领域,对于TOD能在多大程度上降低城市居民小汽车出行距离依然有待明确。基于南京市城市居民出行调查数据,采用家庭所在地800 m范围内有无地铁站点定义TOD中“T”因素,以综合密度、混合度、步行评分作为“D”因素。在控制个人社会经济属性和家庭所在地位置因素的基础上,建立Tobit模型,研究TOD对居民小汽车出行距离的减量影响。通过划分TOD、only-T、only-D、non-TOD等4类家庭居住地,分析边际效应发现,当“T”因素从无地铁站点变为有地铁站点时,小汽车出行距离降低19.6%;“D”因素每增加0.01,小汽车出行距离平均下降0.7%;当“T”与“D”因素综合作用时,小汽车出行距离下降43.4%。依据上述研究结论,提出从only-T向TOD、从non-TOD向only-D转变的规划建议。


关键词: 以公共交通为导向的开发,  小汽车出行距离,  步行评分,  Tobit模型,  边际效应 
变量系数tp边际效应
常数项-6.392-4.6500.000 ***
控制变量家庭学龄前儿童数2.3923.8390.000 ***0.250
家庭中小学生数1.9883.9720.000 ***0.208
家庭非机动车数-2.278-10.0440.000 ***-0.238
家庭所在地到新街口的距离
是否有驾照(有,参照标准为无)13.40218.3410.000 ***1.399
是否有公交卡(有,参照标准为无)-2.631-4.8440.000 ***-0.274
家庭年收入(中等,参照标准为低)5.8477.9860.000 ***0.610
家庭年收入(高,参照标准为低)9.37910.4460.000 ***0.979
性别(女,参照标准为男)-3.009-6.0780.000 ***-0.314
受教育程度(高,参照标准为低)0.9491.7540.079 *0.099
工作类型(上班,参照标准为其他)3.2793.1360.002 **0.342
工作类型(上学,参照标准为其他)7.6796.5650.000 ***0.802
TOD变量T-1.881-2.8790.004 **-0.196
D-6.716-4.8380.000 ***-0.701
Table 1 Estimated coefficients of the model
Fig.1 Spatial distribution of the four types of households
Fig.2 Residential built environment of four typical households
家庭类型小汽车平均出行距离/mT值平均D值家庭数量(比例/%)个人数量(比例/%)
TOD1 31810.57202(10.8)556(10.7)
only-T2 32410.26162(8.6)451(8.7)
only-D1 69200.58368(19.6)1 010(19.4)
non-TOD3 34700.231 147(61.0)3 184(61.2)
Table 2 Average car kilometers traveled of the four types of households
Fig.3 Effects of TOD on car kilometers traveled under different scenarios
[1]   张晓春, 田锋, 吕国林, 等. 深圳市TOD框架体系及规划策略[J]. 城市交通, 2011, 9(3): 37-44. DOI:10. 3969/j.issn.1672-5328.2011.03.006
ZHANG X C, TIAN F, LYU G L, et.al. Transit-oriented development framework and planning strategies in Shenzhen[J]. Urban Transport of China, 2011, 9(3): 37-44. DOI:10.3969/j.issn. 1672-5328.2011.03.006
doi: 10.3969/j.issn. 1672-5328.2011.03.006
[2]   邵源, 田锋, 吕国林, 等. 深圳市TOD规划管理与实践[J]. 城市交通, 2011, 9(2): 60-66, 21. DOI:10. 3969/j.issn.1672-5328.2011.02.010
SHAO Y, TIAN F, LYU G L, et.al. Transit-oriented development planning and management practice in Shenzhen[J]. Urban Transport of China, 2011, 9(2): 60-66, 21. DOI:10.3969/j.issn.1672-5328. 2011.02.010
doi: 10.3969/j.issn.1672-5328. 2011.02.010
[3]   CHATMAN D G. Does TOD need the T: On the importance of factors other than rail access[J]. Journal of the American Planning Association, 2013, 79(1): 17-31. DOI:10.1080/01944363. 2013.791008
doi: 10.1080/01944363. 2013.791008
[4]   EWING R, CERVERO R. Travel and the built environment: A meta-analysis[J]. Journal of the American Planning Association, 2010, 76(3): 265-294. DOI:10.1080/01944361003766766
doi: 10.1080/01944361003766766
[5]   王有为. 适于中国城市的TOD规划理论研究[J]. 城市交通, 2016, 14(6): 40-48. DOI:10.13813/j.cn11-5141/u.2016.0607
WANG Y W. Suitability of TOD planning theory for Chinese cities[J]. Urban Transport of China, 2016, 14(6): 40-48. DOI:10.13813/j.cn11-5141/u.2016. 0607
doi: 10.13813/j.cn11-5141/u.2016. 0607
[6]   MILLER E J, IBRAHIM A. Urban form and vehicular travel: Some empirical findings[J]. Transportation Research Record: Journal of the Transportation Research Board, 1998, 1617(1): 18-27. DOI:10.3141/1617-03
doi: 10.3141/1617-03
[7]   KUMAPLEY R K, FRICKER J D. Review of methods for estimating vehicle miles traveled[J]. Transportation Research Record: Journal of the Transportation Research Board, 1996, 1551(1): 59-66. DOI:10.1177/0361198196155100108
doi: 10.1177/0361198196155100108
[8]   CALTHORPE P. The Next American Metropolis: Ecology, Community, and the American Dream[M]. New York: Princeton Architectural Press, 1993.
[9]   CERVERO R. Transit-Oriented Development in the United States: Experiences, Challenges, and Prospects [M]. Washington: Transportation Research Board, 2004.
[10]   U.S. Maryland Department of Transportation. Glendening from the Transit-oriented Development Task Force[R].Baltimore: U. S. Maryland Transportation Authority, 2000. doi:10.3141/1731-09
doi: 10.3141/1731-09
[11]   CERVERO R, KOCKELMAN K. Travel demand and the 3Ds: Density, diversity, and design[J]. Transportation Research Part D (Transport and Environment), 1997, 2(3): 199-219. DOI:10.1016/S1361-9209(97)00009-6
doi: 10.1016/S1361-9209(97)00009-6
[12]   YUE Y, ZHUANG Y, YEH A G O, et al. Measurements of POI-based mixed use and their relationships with neighbourhood vibrancy[J]. International Journal of Geographical Information Science, 2016, 31(4): 658-675. DOI:10.1080/13658816.2016.1220561
doi: 10.1080/13658816.2016.1220561
[13]   REUSSER D E, LOUKOPOULOS P, STAUFFACHER M, et al. Classifying railway stations for sustainable transitions-balancing node and place functions[J]. Journal of Transport Geography, 2008, 16(3): 191-202. DOI:10.1016/j.jtrangeo.2007.05.004
doi: 10.1016/j.jtrangeo.2007.05.004
[14]   CARR L J, DUNSIGER S I, MARCUS B H. Walk scoreTM as a global estimate of neighborhood walkability[J]. American Journal of Preventive Medicine, 2010, 39(5): 460-463. DOI:10.1016/j.amepre.2010.07.007 .
doi: 10.1016/j.amepre.2010.07.007
[15]   CARR L J, DUNSIGER S I, MARCUS B H. Validation of walk score for estimating access to walkable amenities [J]. British Journal of Sports Medicine, 2011, 45(14): 1144-1148. DOI:10.1136/bjsm.2009.069609
doi: 10.1136/bjsm.2009.069609
[16]   MANAUGH K, AHMED E. Validating walkability indices: How do different households respond to the walkability of their neighborhood?[J]. Transportation Research Part D (Transport and Environment), 2011, 16(4): 309-315. DOI:10.1016/j.trd.2011.01.009
doi: 10.1016/j.trd.2011.01.009
[17]   TOBIN J. The Application of Multivariate Probit Analysis to Economic Survey Data[R]. New Haven: Cowles Foundation for Research in Economics, Yale University, 1955.
[18]   中国城市轨道交通协会. 城市轨道交通2019年度统计和分析报告[R]. 北京: 中国城市轨道交通协会, 2020.
China Urban Rail Transit Association. 2019 Annual Statistical Analysis Report of China′s Urban Rail Transit [R]. Beijing: China Urban Rail Transit Association, 2020.
[19]   宇恒可持续交通研究中心. 2016年中国城市轨道TOD发展指数报告[R]. 北京: 宇恒可持续交通研究中心, 2017. doi:10.1007/s40864-017-0054-4
Yuheng Sustainable Transportation Research Center. 2016 China′s Urban Rail Transit TOD Development Index Report [R]. Beijing: Yuheng Sustainable Transportation Research Center, 2017. doi:10.1007/s40864-017-0054-4
doi: 10.1007/s40864-017-0054-4
[1] Biao XU,Qingchang LU,Pengcheng XU,Xin CUI,Changhao DU. A study on group difference between low-carbon transportation intention and consistent behavior[J]. Journal of Zhejiang University (Science Edition), 2023, 50(3): 378-390.
[2] YUE Qi. Two-sided matching considering uncertain psychological behavior with score information[J]. Journal of Zhejiang University (Science Edition), 2016, 43(2): 242-246.