Residential density and transportation emissions: Examining the connection by addressing spatial autocorrelation and self-selection

Hong, Jinhyun; Shen, Qing

doi:10.1016/j.trd.2013.03.006

Cited by 48 publications

(24 citation statements)

References 7 publications

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“…Apart from the aforementioned characteristics, attitude and preference characteristics have received more attention recently as self-selection effects [41,[45][46][47][48]. A variety of outcomes from self-selection effect have been empirically examined, ranging from travel model choice [47,[49][50][51]] to transportation energy consumption and emissions [41,45]. With rapid urbanization, car ownership is still growing constantly in small and mid-sized cities in China.…”

Section: Other Factors Influencing Car Ownership and Usementioning

confidence: 99%

Built Environment and Parking Availability: Impacts on Car Ownership and Use

2018

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Along with urbanization and economic development, the number of private cars has increased rapidly in recent years in China, which contributes to concerns about traffic congestion, hard parking, energy consumption, and emissions. This study aims to investigate the joint effect of built environment and parking availability on car ownership and use based on a household travel survey conducted in Changchun, China. The binary logistic model was first employed to investigate the determinants of the car ownership in Changchun. Next, this study examined the potential impacts of the built environment and parking availability on car use for the journey to work. The result shows that built environment and parking availability can be both significantly associated with car ownership and use after controlling for the socioeconomic characteristics. Moreover, in contrast with the model ignoring the parking availability, the model for car use considering the joint effect fit the data better. The results indicate that car dependency depends on the joint effect of the built environment and parking availability. These results suggest that transit-oriented urban expansion and compact land use can contribute to reducing car commuting. Meanwhile, parking restrictions at both trip start and end would be effective for sustainable transport because parking oversupply could encourage more car dependency.

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Section: Other Factors Influencing Car Ownership and Usementioning

confidence: 99%

Built Environment and Parking Availability: Impacts on Car Ownership and Use

2018

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“…After the spatial heterogeneity is taken into consideration, the model performs better than the simple models [24]. However, the spatial relationship between groups cannot be captured with a multilevel model, which is also viewed as a way to display the effect of location [28]. Hong et al [39] proposed a unified analytical framework to examine the spatial effects when exploring the influences of the built environment on VMT and found that the model considering spatial effects fit the data better.…”

Section: Spatial Effects In City Contextmentioning

confidence: 99%

“…Second, few prior studies exploring the impacts of the built environment on car dependency take into account spatial autocorrelation, which might lead to misunderstanding the role the built environment plays. The spatial autocorrelation, which has been studied in spatial economics by several approaches and proven to create an effect on travel behavior [26][27][28], is still rarely considered in existing studies.…”

Section: Introductionmentioning

confidence: 99%

Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun

et al. 2018

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Concerns about transportation energy consumption and emissions force urban planners and policy makers to pay more attention to the effects of car ownership and use on the environment in China. However, few studies have investigated the relationship between the built environment and car ownership and use in China, especially in mid-sized and small cities. This study uses Changchun, China as a case study and examines the potential impacts of the built environment and socio-demographics on car ownership and use for commuting simultaneously using Bayesian multilevel binary logistic models. Furthermore, the spatial autocorrelation of car ownership and use is recognized across traffic analysis zones (TAZs), which are specifically represented by the conditional autoregressive (CAR) model. The estimated results indicate that socio-demographic characteristics have significant effects on car ownership and use. Moreover, the built environment measured at the TAZ level still shows a significant association with other factors controlled. Specifically, it suggests that denser residential density, compact land use, better transit services and street connectivity can reduce car dependency more effectively. This study provides new insights into how the built environment influences the car ownership and use, which can be useful for urban planners and policy makers to develop strategies for reducing car dependency.

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“…Ignoring the spatial heterogeneity in travel behavior studies generally results in inconsistent parameter estimation. To accommodate the spatial context, the multilevel/hierarchical modeling framework has been recently applied [9,[12][13][14][15][16]. Hong et al [9] and Zhang et al [14] examined the effects of built environment factors at home location on VMT by employing Bayesian multilevel model.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Role of Built Environment in Reducing Vehicle Miles Traveled Accounting for Spatial Heterogeneity

et al. 2014

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Abstract:In recent years, increasing concerns over climate change and transportation energy consumption have sparked research into the influences of urban form and land use patterns on motorized travel, notably vehicle miles traveled (VMT). However, empirical studies provide mixed evidence of the influence of the built environment on travel. In particular, the role of density after controlling for the confounding factors (e.g., land use mix, average block size, and distance from CBD) still remains unclear. The object of this study is twofold. First, this research provides additional insights into the effects of built environment factors on the work-related VMT, considering urban form measurements at both the home location and workplace simultaneously. Second, a cross-classified multilevel model using Bayesian approach is applied to account for the spatial heterogeneity across spatial units. Using Washington DC as our study area, the home-based work tour in the AM peak hours is used as the analysis unit. Estimation results confirmed the important role that the built environment at both home and workplace plays in affecting work-related VMT. In particular, the results reveal that densities at the workplace have more important roles than that at home location. These findings confirm that urban planning and city design should be part of the solution in stabilizing global climate and energy consumption.

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Residential density and transportation emissions: Examining the connection by addressing spatial autocorrelation and self-selection

Cited by 48 publications

References 7 publications

Built Environment and Parking Availability: Impacts on Car Ownership and Use

Built Environment and Parking Availability: Impacts on Car Ownership and Use

Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun

Understanding the Role of Built Environment in Reducing Vehicle Miles Traveled Accounting for Spatial Heterogeneity

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