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29-08-2022

Exploring the causal effects of the built environment on travel behavior: a unique randomized experiment in Shanghai

Authors: Faan Chen, Adriano Borges Costa

Published in: Transportation | Issue 1/2024

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Abstract

Experimental designs have been recognized as the gold standard for establishing causal mechanisms. However, the application of such designs is complicated by factors such as excessive costs, time consumption, ethical concerns, and political impossibility. Nevertheless, the Chinese government’s replacement housing efforts provide a unique randomized experiment for exploring the causal effects of the built environment on travel behavior. Accordingly, based on a large-scale survey on travel patterns under an experimental design in Shanghai, this study employs a two-step modeling approach, involving logit and Tobit models, to identify the built environment’s effects on auto ownership and vehicle kilometers traveled (VKT). We found that transit service improvements play a stronger role in reducing auto-drive than compact and diverse land-use characteristics. Increasing residential and employment density, as well as land-use mix, discourages car ownership, which in turn reduces VKT, but with lower elasticities than transportation system variables. The findings provide additional evidence and referential estimate for how land-use and transport strategies and policies designed to create a compact, mixed-use, and highly accessible built environment can be used in reducing auto driving. This study expands the VKT reduction elasticities’ database regarding the built environment across global spatial contexts, serving as a model for similar studies elsewhere in the world.

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For the compensation procedure for replacement housing, see < Regulations of Shanghai Municipality on the Expropriation and Compensation of Houses on State-owned Land > (http://www.shanghai.gov.cn/nw2/nw2314/nw2319/nw2404/nw28669/nw28670/u26aw29878.html).

Households have a certain level of knowledge about where replacement houses are generally located (in suburban areas, in large-scale housing programs), and it affects household choice when they must choose one among the three compensation options. At this point, household socioeconomic characteristics affect the choice. This way, households that do not select the third option can be seen as no-compliers (Angrist and Pischke 2008), as they were assigned to be relocated but decided not to take the third form of treatment. Once they opt to get the third form of treatment, households know nothing about the specific location they will be assigned to reside in, and suburban areas are heterogeneous enough for our analysis. Their final locations of housing are assigned through a lottery system operated by the government and are not correlated with household socioeconomic characteristics. This way, our results can be seen as “Local Average Treatment Effect” (LATE) (Angrist and Pischke 2008), also known as “Complier Average Causal Effect” (CACE). LATE is the average treatment effect for the Compliers. It is important to note that our LATE results are unbiased and not affected by self-selection.

There are currently six indemnificatory housing bases in Shanghai: Jiangqiao (in Jiading District), Sijing (in Songjiang District), Pujiang (in Minhang District), Sanlin (in Pudong District), Zhoukang (in Pudong District), and Gucun (in Baoshan District).

Mature housing community means that the neighborhoods within which are built upon ten or more years with completed public and commercial facilities and services at the time of the survey.

JA = Jin’an neighborhood, JL = Jinlai neighborhood, JZ = Jinzhong neighborhood; XK-1 = Xinkai 1st village, XK-2 = Xinkai 2nd village; PX = Puhang new-town 4th neighborhood, PB = Pujiang Baodi neighborhood; HT = Huiteng neighborhood, OF = Oufeng neighborhood; HJ = Haorizi Large Jiayuan neighborhood A, JX-1 = Juquan new-town 1st neighborhood 1, JX-4 = Juquan new-town 4th neighborhood, JX-7 = Juquan new-town 7th neighborhood.

The allocation unit of resettlement is set by the government department, which may be neighborhood, housing base, neighborhood type (e.g., TOD vs. non-TOD), or others. This information is not open to the public. As thus, it is reasonable that there existed some differences in socio-economics between separate neighborhoods if the residents were allocated by other units.

Hong Kong, officially the Hong Kong Special Administrative Region of the People's Republic of China (HKSAR), is a city and special administrative region of China.

Aditjandra, P.T., Cao, X., Mulley, C.: Understanding neighbourhood design impact on travel behaviour: an application of structural equations model to a British metropolitan data. Transp. Res. Part A Policy Pract. 46(1), 22–32 (2012). https://doi.org/10.1016/j.tra.2011.09.001CrossRef

Ahmad, S., Puppim de Oliveira, J.A.: Determinants of urban mobility in India: lessons for promoting sustainable and inclusive urban transportation in developing countries. Transp. Policy 50, 106–114 (2016). https://doi.org/10.1016/j.tranpol.2016.04.014CrossRef

Angrist, J.D., Pischke, J.-S.: Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press (2008). https://doi.org/10.1515/9781400829828CrossRef

Baar, J., Romppel, M., Igel, U., Brähler, E., Grande, G.: The independent relations of both residential self-selection and the environment to physical activity. Int. J. Environ. Health Res. 25(3), 288–298 (2015). https://doi.org/10.1080/09603123.2014.938029CrossRef

Babbie, E.R.: The Practice of Social Research, 14th edn. Cengage Learning (2015)

Bhat, C.R., Eluru, N.: A copula-based approach to accommodate residential self-selection effects in travel behavior modeling. Transp. Res. Part B Methodol. 43(7), 749–765 (2009). https://doi.org/10.1016/j.trb.2009.02.001CrossRef

Bhat, C.R., Astroza, S., Sidharthan, R., Alam, M.J.B., Khushefati, W.H.: A joint count-continuous model of travel behavior with selection based on a multinomial probit residential density choice model. Transp. Res. Part B Methodol. 68, 31–51 (2014). https://doi.org/10.1016/j.trb.2014.05.004CrossRef

Bhat, C.R., Astroza, S., Bhat, A.C., Nagel, K.: Incorporating a multiple discrete-continuous outcome in the generalized heterogeneous data model: application to residential self-selection effects analysis in an activity time-use behavior model. Transp. Res. Part B Methodol. 91, 52–76 (2016). https://doi.org/10.1016/j.trb.2016.03.007CrossRef

Biying, Y., Zhang, J., Fujiwara, A.: Analysis of the residential location choice and household energy consumption behavior by incorporating multiple self-selection effects. Energy Policy 46, 319–334 (2012). https://doi.org/10.1016/j.enpol.2012.03.067CrossRef

Brownstone, D., Golob, T.F.: The impact of residential density on vehicle usage and energy consumption. J. Urban Econ. 65(1), 91–98 (2009). https://doi.org/10.1016/j.jue.2008.09.002CrossRef

Burbidge, S.K., Goulias, K.G.: Evaluating the impact of neighborhood trail development on active travel behavior and overall physical activity of suburban residents. Transp. Res. Rec. 2135(1), 78–86 (2009). https://doi.org/10.3141/2135-10CrossRef

Cao, X.: Disentangling the influence of neighborhood type and self-selection on driving behavior: an application of sample selection model. Transportation 36(2), 207–222 (2009). https://doi.org/10.1007/s11116-009-9189-9CrossRef

Cao, X.: Exploring causal effects of neighborhood type on walking behavior using stratification on the propensity score. Environ. Plan A Econ. Space 42(2), 487–504 (2010). https://doi.org/10.1068/a4269CrossRef

Cao, X.: Examining the impacts of neighborhood design and residential self-selection on active travel: a methodological assessment. Urban Geogr. 36(2), 236–255 (2015). https://doi.org/10.1080/02723638.2014.956420CrossRef

Cao, J., Cao, X.: The impacts of LRT, neighbourhood characteristics, and self-selection on auto ownership: evidence from Minneapolis-St. Paul. Urban Stud. 51(10), 2068–2087 (2013). https://doi.org/10.1177/0042098013505887CrossRef

Cao, X., Fan, Y.: Exploring the influences of density on travel behavior using propensity score matching. Environ. Plan. B Plan. Des. 39(3), 459–470 (2012). https://doi.org/10.1068/b36168CrossRef

Cao, X., Schoner, J.: The influence of light rail transit on transit use: an exploration of station area residents along the Hiawatha line in Minneapolis. Transp. Res. Part A Policy Pract. 59, 134–143 (2014). https://doi.org/10.1016/j.tra.2013.11.001CrossRef

Cao, X., Yang, W.: Examining the effects of the built environment and residential self-selection on commuting trips and the related CO₂ emissions: an empirical study in Guangzhou, China. Transp. Res. Part D Transp. Environ. 52, 480–494 (2017). https://doi.org/10.1016/j.trd.2017.02.003CrossRef

Cao, X., Mokhtarian, P.L., Handy, S.L.: Examining the impacts of residential self-selection on travel behaviour: a focus on empirical findings. Transp. Rev. 29(3), 359–395 (2009a). https://doi.org/10.1080/01441640802539195CrossRef

Cao, X., Mokhtarian, P.L., Handy, S.L.: The relationship between the built environment and nonwork travel: a case study of Northern California. Transp. Res. Part A Policy Pract. 43(5), 548–559 (2009b). https://doi.org/10.1016/j.tra.2009.02.001CrossRef

Cao, X., Xu, Z., Fan, Y.: Exploring the connections among residential location, self-selection, and driving: propensity score matching with multiple treatments. Transp. Res. Part A Policy Pract. 44(10), 797–805 (2010). https://doi.org/10.1016/j.tra.2010.07.010CrossRef

Cervero, R.: Transit-oriented development’s ridership bonus: a product of self-selection and public policies. Environ. Plan. A Econ. Space 39(9), 2068–2085 (2007). https://doi.org/10.1068/a38377CrossRef

Chen, F., Wu, J., Chen, X., Wang, J.: Vehicle kilometers traveled reduction impacts of Transit-Oriented Development: evidence from Shanghai City. Transp. Res. Part D Transp. Environ. 55, 227–245 (2017). https://doi.org/10.1016/j.trd.2017.07.006CrossRef

Chen, F., Wu, J., Chen, X., Nielsen, C.P.: Disentangling the impacts of the built environment and residential self-selection on travel behavior: an empirical study in the context of diversified housing types. Cities (2021). https://doi.org/10.1016/j.cities.2021.103285CrossRef

Clark, A.F., Scott, D.M.: Does the social environment influence active travel? An investigation of walking in Hamilton, Canada. J. Transp. Geogr. 31, 278–285 (2013). https://doi.org/10.1016/j.jtrangeo.2013.06.005CrossRef

De Gruyter, C., Rose, G., Currie, G.: Travel plans for new residential developments: measuring self-selection effects to better understand travel behavior impacts. Transp. Res. Rec. 2564(1), 60–69 (2016). https://doi.org/10.3141/2564-07CrossRef

Deming, W.E.: Sample Design in Business Research, vol. 23. Wiley (1991)

Dendup, T., Astell-Burt, T., Feng, X.: Residential self-selection, perceived built environment and type 2 diabetes incidence: a longitudinal analysis of 36,224 middle to older age adults. Health Place 58, 102154 (2019). https://doi.org/10.1016/j.healthplace.2019.102154CrossRef

Deng, Y., Yan, Y.: Propensity score weighting with generalized boosted models to explore the effects of the built environment and residential self-selection on travel behavior. Transp. Res. Rec. 2673(4), 373–383 (2019). https://doi.org/10.1177/0361198119837153CrossRef

Dillon, H.S., Saphores, J.-D., Boarnet, M.G.: The impact of urban form and gasoline prices on vehicle usage: evidence from the 2009 National Household Travel Survey. Res. Transp. Econ. 52, 23–33 (2015). https://doi.org/10.1016/j.retrec.2015.10.006CrossRef

Dubin, J.A., McFadden, D.L.: An econometric analysis of residential electric appliance holdings and consumption. Econometrica 52(2), 345–362 (1984). https://doi.org/10.2307/1911493CrossRef

Eluru, N., Bhat, C.R., Pendyala, R.M., Konduri, K.C.: A joint flexible econometric model system of household residential location and vehicle fleet composition/usage choices. Transportation 37(4), 603–626 (2010). https://doi.org/10.1007/s11116-010-9271-3CrossRef

Etminani-Ghasrodashti, R., Ardeshiri, M.: The impacts of built environment on home-based work and non-work trips: an empirical study from Iran. Transp. Res. Part A Policy Pract. 85, 196–207 (2016). https://doi.org/10.1016/j.tra.2016.01.013CrossRef

Ettema, D., Nieuwenhuis, R.: Residential self-selection and travel behaviour: what are the effects of attitudes, reasons for location choice and the built environment? J. Transp. Geogr. 59, 146–155 (2017). https://doi.org/10.1016/j.jtrangeo.2017.01.009CrossRef

Ewing, R., Cervero, R.: Travel and the built environment. J. Am. Plan. Assoc. 76(3), 265–294 (2010). https://doi.org/10.1080/01944361003766766CrossRef

Ewing, R., Hamidi, S., Grace, J.B.: Compact development and VMT—environmental determinism, self-selection, or some of both? Environ. Plan. B. Plan. Des. 43(4), 737–755 (2015). https://doi.org/10.1177/0265813515594811CrossRef

Gim, T.-H.T.: Examining the effects of residential self-selection on internal and external validity: an interaction moderation analysis using structural equation modeling. Transp. Lett. 11(5), 275–286 (2019). https://doi.org/10.1080/19427867.2017.1338544CrossRef

Guan, X., Wang, D.: Residential self-selection in the built environment-travel behavior connection: whose self-selection? Transp. Res. Part D Transp. Environ. 67, 16–32 (2019). https://doi.org/10.1016/j.trd.2018.10.015CrossRef

Hammond, D.:. Residential location and commute mode choice. Doctoral dissertation, University of Wales, Cardiff. Shanghai Statistical Bureau, 2011. Shanghai Statistical Yearbook 2011. Shanghai Press, Shanghai (2015). http://www.statssh.gov.cn/data/toTjnj.xhtml?y%BC2011e

Handy, S., Cao, X., Mokhtarian, P.L.: Self-selection in the relationship between the built environment and walking: empirical evidence from Northern California. J. Am. Plan. Assoc. 72(1), 55–74 (2006). https://doi.org/10.1080/01944360608976724CrossRef

Heinen, E., Mackett, R., van Wee, B., Ogilvie, D., Panter, J.: Residential self-selection in quasi-experimental and natural experimental studies. An extended conceptualization of the relationship between the built environment and travel behavior. J. Transp. Land Use 11(1), 939–959 (2018)CrossRef

Heres-Del-Valle, D., Niemeier, D.: CO₂ emissions: are land-use changes enough for California to reduce VMT? Specification of a two-part model with instrumental variables. Transp. Res. Part B Methodol. 45(1), 150–161 (2011). https://doi.org/10.1016/j.trb.2010.04.001CrossRef

Holupka, S., Newman, S.J.: The Effects of homeownership on children’s outcomes: real effects or self-selection? Real Estate Econ. 40(3), 566–602 (2012). https://doi.org/10.1111/j.1540-6229.2012.00330.xCrossRef

Hong, J., Shen, Q., Zhang, L.: How do built-environment factors affect travel behavior? A spatial analysis at different geographic scales. Transportation 41(3), 419–440 (2014). https://doi.org/10.1007/s11116-013-9462-9CrossRef

Huang, X., Cao, X., Cao, J.: The association between transit access and auto ownership: evidence from Guangzhou, China. Transp. Plan. Technol. 39(3), 269–283 (2016a). https://doi.org/10.1080/03081060.2016.1142223CrossRef

Huang, X., Cao, X., Cao, X., Yin, J.: How does the propensity of living near rail transit moderate the influence of rail transit on transit trip frequency in Xi’an? J. Transp. Geogr. 54, 194–204 (2016b). https://doi.org/10.1016/j.jtrangeo.2016.05.016CrossRef

Jarass, J., Scheiner, J.: Residential self-selection and travel mode use in a new inner-city development neighbourhood in Berlin. J. Transp. Geogr. 70, 68–77 (2018). https://doi.org/10.1016/j.jtrangeo.2018.05.018CrossRef

Jin, J.: The effects of labor market spatial structure and the built environment on commuting behavior: considering spatial effects and self-selection. Cities 95, 102392 (2019). https://doi.org/10.1016/j.cities.2019.102392CrossRef

Kamruzzaman, M., Washington, S., Baker, D., Brown, W., Giles-Corti, B., Turrell, G.: Built environment impacts on walking for transport in Brisbane, Australia. Transportation 43(1), 53–77 (2016). https://doi.org/10.1007/s11116-014-9563-0CrossRef

Kim, J., Brownstone, D.: The impact of residential density on vehicle usage and fuel consumption: evidence from national samples. Energy Econ. 40, 196–206 (2013). https://doi.org/10.1016/j.eneco.2013.06.012CrossRef

Klinger, T., Lanzendorf, M.: Moving between mobility cultures: what affects the travel behavior of new residents? Transportation 43(2), 243–271 (2016). https://doi.org/10.1007/s11116-014-9574-xCrossRef

Knuiman, M.W., Christian, H.E., Divitini, M.L., Foster, S.A., Bull, F.C., Badland, H.M., Giles-Corti, B.: A longitudinal analysis of the influence of the neighborhood built environment on walking for transportation: the RESIDE study. Am. J. Epidemiol. 180(5), 453–461 (2014). https://doi.org/10.1093/aje/kwu171CrossRef

Kumar, P.P., Sekhar, C.R., Parida, M.: Residential dissonance in TOD neighborhoods. J. Transp. Geogr. 72, 166–177 (2018). https://doi.org/10.1016/j.jtrangeo.2018.09.005CrossRef

Litman, T.: Land Use Impacts on Transport: How Land Use Factors Affect Travel Behavior (2017)

Liu, C., Sun, Y., Chen, Y., Susilo, Y.O.: The effect of residential housing policy on car ownership and trip chaining behaviour in Hangzhou, China. Transp. Res. Part D Transp. Environ. 62, 125–138 (2018). https://doi.org/10.1016/j.trd.2018.02.008CrossRef

Lu, Y., Chen, L., Yang, Y., Gou, Z.: The association of built environment and physical activity in older adults: using a citywide public housing scheme to reduce residential self-selection bias. Int. J. Environ. Res. Public Health 15, 9 (2018). https://doi.org/10.3390/ijerph15091973CrossRef

Ma, J., Liu, Z., Chai, Y.: The impact of urban form on CO2 emission from work and non-work trips: the case of Beijing, China. Habitat Int. 47, 1–10 (2015). https://doi.org/10.1016/j.habitatint.2014.12.007CrossRef

Macfarlane, G.S., Garrow, L.A., Mokhtarian, P.L.: The influences of past and present residential locations on vehicle ownership decisions. Transp. Res. Part A Policy Pract. 74, 186–200 (2015). https://doi.org/10.1016/j.tra.2015.01.005CrossRef

McKee, R., Mutrie, N., Crawford, F., Green, B.: Promoting walking to school: results of a quasi-experimental trial. J. Epidemiol. Community Health 61(9), 818 (2007). https://doi.org/10.1136/jech.2006.048181CrossRef

Mokhtarian, P.L., Cao, X.: Examining the impacts of residential self-selection on travel behavior: a focus on methodologies. Transp. Res. Part B Methodol. 42(3), 204–228 (2008). https://doi.org/10.1016/j.trb.2007.07.006CrossRef

Munshi, T.: Built environment and mode choice relationship for commute travel in the city of Rajkot, India. Transp. Res. Part D Transp. Environ. 44, 239–253 (2016). https://doi.org/10.1016/j.trd.2015.12.005CrossRef

Næss, P.: Residential location, travel, and energy use in the Hangzhou metropolitan area. Journal of Transport and Land Use 3(3), 27–59 (2010)CrossRef

Næss, P.: Tempest in a teapot: the exaggerated problem of transport-related residential self-selection as a source of error in empirical studies. J. Transp. Land Use 7(3), 57–79 (2014)CrossRef

Nasri, A., Carrion, C., Zhang, L., Baghaei, B.: Using propensity score matching technique to address self-selection in transit-oriented development (TOD) areas. Transportation 47(1), 359–371 (2020). https://doi.org/10.1007/s11116-018-9887-2CrossRef

National Research Council: Driving and the Built Environment: The Effects of Compact Development on Motorized Travel, Energy Use, and CO2 Emissions (030915054X). Transportation Research Board Special Report 298, Issue (2010)

Ortúzar, J.D.D., Armoogum, J., Madre, J.L., Potier, F.: Continuous mobility surveys: the state of practice. Transp. Rev. 31(3), 293–312 (2011). https://doi.org/10.1080/01441647.2010.510224CrossRef

Ottelin, J., Heinonen, J., Junnila, S.: Rebound effects for reduced car ownership and driving. In: Nordic Experiences of Sustainable Planning, pp. 263–283. Routledge (2017)

Paleti, R., Bhat, C.R., Pendyala, R.M.: Integrated model of residential location, work location, vehicle ownership, and commute tour characteristics. Transp. Res. Rec. 2382(1), 162–172 (2013). https://doi.org/10.3141/2382-18CrossRef

Park, K., Ewing, R., Scheer, B.C., Ara-Khan, S.S.: Travel behavior in TODs vs. non-TODs: using cluster analysis and propensity score matching. Transp. Res. Rec. 2672(6), 31–39 (2018). https://doi.org/10.1177/0361198118774159CrossRef

Phani Kumar, P., Ravi Sekhar, C., Parida, M.: Identification of neighborhood typology for potential transit-oriented development. Transp. Res. Part D Transp. Environ. 78, 102186 (2020). https://doi.org/10.1016/j.trd.2019.11.015CrossRef

Pinjari, A.R., Pendyala, R.M., Bhat, C.R., Waddell, P.A.: Modeling the choice continuum: an integrated model of residential location, auto ownership, bicycle ownership, and commute tour mode choice decisions. Transportation 38(6), 933 (2011). https://doi.org/10.1007/s11116-011-9360-yCrossRef

Prevedouros, P.D.: Associations of personality characteristics with transport behavior and residence location decisions. Transp. Res. Part A Policy Pract. 26(5), 381–391 (1992). https://doi.org/10.1016/0965-8564(92)90002-OCrossRef

Scheiner, J., Holz-Rau, C.: Changes in travel mode use after residential relocation: a contribution to mobility biographies. Transportation 40(2), 431–458 (2013). https://doi.org/10.1007/s11116-012-9417-6CrossRef

Schwanen, T., Mokhtarian, P.L.: What if you live in the wrong neighborhood? The impact of residential neighborhood type dissonance on distance traveled. Transp. Res. Part D Transp. Environ. 10(2), 127–151 (2005). https://doi.org/10.1016/j.trd.2004.11.002CrossRef

Shadish, W., Cook, T.D., Campbell, D.T.: Experimental and Quasi-experimental Designs for Generalized Causal Inference. Houghton Mifflin, Boston (2002)

Shirgaokar, M.: Expanding cities and vehicle use in India: differing impacts of built environment factors on scooter and car use in Mumbai. Urban Stud. 53(15), 3296–3316 (2015). https://doi.org/10.1177/0042098015608050CrossRef

Squarize Chagas, A. L., Malvezzi Rocha, G.: Housing Program and Social Conditions Impact: Evidences from “Minha Casa Minha Vida” Program Lotteries in Brazil (2019)

Stevens, M.R.: Does compact development make people drive less? J. Am. Plan. Assoc. 83(1), 7–18 (2017). https://doi.org/10.1080/01944363.2016.1240044CrossRef

Sugiyama, T., Giles-Corti, B., Summers, J., du Toit, L., Leslie, E., Owen, N.: Initiating and maintaining recreational walking: a longitudinal study on the influence of neighborhood green space. Prev. Med. 57(3), 178–182 (2013). https://doi.org/10.1016/j.ypmed.2013.05.015CrossRef

Susilo, Y.O., Liu, C.: The influence of parents’ travel patterns, perceptions and residential self-selectivity to their children travel mode shares. Transportation 43(2), 357–378 (2016)CrossRef

Thøgersen, J.: Promoting public transport as a subscription service: effects of a free month travel card. Transp. Policy 16(6), 335–343 (2009). https://doi.org/10.1016/j.tranpol.2009.10.008CrossRef

Tobin, J.: Estimation of relationships for limited dependent variables. Econometrica 26(1), 24–36 (1958). https://doi.org/10.2307/1907382CrossRef

Van Acker, V., Witlox, F.: Car ownership as a mediating variable in car travel behaviour research using a structural equation modelling approach to identify its dual relationship. J. Transp. Geogr. 18(1), 65–74 (2010). https://doi.org/10.1016/j.jtrangeo.2009.05.006CrossRef

Van Acker, V., Mokhtarian, P.L., Witlox, F.: Car availability explained by the structural relationships between lifestyles, residential location, and underlying residential and travel attitudes. Transp. Policy 35, 88–99 (2014). https://doi.org/10.1016/j.tranpol.2014.05.006CrossRef

van de Coevering, P., Maat, K., van Wee, B.: Multi-period research designs for identifying causal effects of built environment characteristics on travel behaviour. Transp. Rev. 35(4), 512–532 (2015). https://doi.org/10.1080/01441647.2015.1025455CrossRef

van de Coevering, P., Maat, K., Kroesen, M., Wee, B., v.: Causal effects of built environment characteristics on travel behaviour: a longitudinal approach. Eur. J. Transp. Infrastruct. Res. (2016). https://doi.org/10.18757/ejtir.2016.16.4.3165CrossRef

van de Coevering, P., Maat, K., van Wee, B.: Residential self-selection, reverse causality and residential dissonance. A latent class transition model of interactions between the built environment, travel attitudes and travel behavior. Transp. Res. Part A Policy Pract. 118, 466–479 (2018). https://doi.org/10.1016/j.tra.2018.08.035CrossRef

van Herick, D., Mokhtarian, P.L.: How much does the method matter? An empirical comparison of ways to quantify the influence of residential self-selection. Travel Behav. Soc. 18, 68–82 (2020). https://doi.org/10.1016/j.tbs.2019.09.002CrossRef

Vernez Moudon, A., Stewart, O.: WSDOT Research Report WA-RD 806.3: Tools for Estimating VMT Reductions from Built Environment Changes [Tech Report]. https://rosap.ntl.bts.gov/view/dot/24385 (2013)

Wang, D., Lin, T.: Residential self-selection, built environment, and travel behavior in the Chinese context. J. Transp. Land Use 7(3), 5–14 (2014). https://doi.org/10.1016/j.habitatint.2021.102401CrossRef

Wang, D., Lin, T.: Built environment, travel behavior, and residential self-selection: a study based on panel data from Beijing, China. Transportation 46(1), 51–74 (2019). https://doi.org/10.1007/s11116-017-9783-1CrossRef

Wang, D., Zhou, M.: The built environment and travel behavior in urban China: a literature review. Transp. Res. Part D Transp. Environ. 52, 574–585 (2017). https://doi.org/10.1016/j.trd.2016.10.031CrossRef

van Wee, B., Cao, J.: Residential self-selection in the relationship between the built environment and travel behavior: a literature review and research agenda. In: Advances in Transport Policy and Planning. Academic Press (2020). https://doi.org/10.1016/bs.atpp.2020.08.004

Yang, S., Fan, Y., Deng, W., Cheng, L.: Do built environment effects on travel behavior differ between household members? A case study of Nanjing, China. Transp. Policy 81, 360–370 (2019). https://doi.org/10.1016/j.tranpol.2017.12.006CrossRef

Zegras, C.: The Built environment and motor vehicle ownership and use: evidence from Santiago de Chile. Urban Stud. 47(8), 1793–1817 (2010). https://doi.org/10.1177/0042098009356125CrossRef

Zhang, J.: Revisiting residential self-selection issues: a life-oriented approach. J. Transp. Land Use 7(3), 29–45 (2014)CrossRef

Zhao, P.: Car use, commuting and urban form in a rapidly growing city: evidence from Beijing. Transp. Plan. Technol. 34(6), 509–527 (2011). https://doi.org/10.1080/03081060.2011.600049CrossRef

Zhou, B., Kockelman, K.M.: Self-selection in home choice: use of treatment effects in evaluating relationship between built environment and travel behavior. Transp. Res. Rec. 2077(1), 54–61 (2008). https://doi.org/10.3141/2077-08CrossRef

Zick, C.D., Hanson, H., Fan, J.X., Smith, K.R., Kowaleski-Jones, L., Brown, B.B., Yamada, I.: Re-visiting the relationship between neighbourhood environment and BMI: an instrumental variables approach to correcting for residential selection bias. Int. J. Behav. Nutr. Phys. Act. 10(1), 27 (2013). https://doi.org/10.1186/1479-5868-10-27CrossRef

Title: Exploring the causal effects of the built environment on travel behavior: a unique randomized experiment in Shanghai
Authors: Faan Chen
Adriano Borges Costa
Publication date: 29-08-2022
Publisher: Springer US
Published in: Transportation / Issue 1/2024
Print ISSN: 0049-4488
Electronic ISSN: 1572-9435
DOI: https://doi.org/10.1007/s11116-022-10325-5

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