Residential self-selection, reverse causality and residential dissonance. A latent class transition model of interactions between the built environment, travel attitudes and travel behavior

2018 ◽  
Vol 118 ◽  
pp. 466-479 ◽  
Author(s):  
Paul van de Coevering ◽  
Kees Maat ◽  
Bert van Wee
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Latent Class ◽  
Transition Model ◽  
Reverse Causality ◽  
Self Selection

scholarly journals Impact of Residential Self-Selection on Low-Carbon Behavior: Evidence from Zhengzhou, China

2019 ◽  
Vol 11 (23) ◽  
pp. 6871 ◽  
Author(s):  
Jingfei Zhang ◽  
Lijun Zhang ◽  
Yaochen Qin ◽  
Xia Wang ◽  
Zhicheng Zheng
Keyword(s):  
Built Environment ◽  
Carbon Emissions ◽  
Travel Behavior ◽  
Energy Use ◽  
Low Carbon ◽  
Self Selection ◽  
Low Carbon Energy ◽  
The Relationship ◽  
Daily Travel ◽  
At Home

Current resident lifestyles pose a significant threat to urban sustainable development. Therefore, low-carbon behavior is receiving increasing attention from scholars and policy makers. Ascertaining residential self-selection is essential in order to study the relationship between the built environment and travel behavior. While several studies have explored the relationship between the urban form, socioeconomic factors, and travel behavior, only a few of them have studied the impact of self-selection on household energy consumption and other forms of consumption, which are also contribute to household carbon emissions. Using large-scale field surveys of 1,485 households and high-resolution images, sourced from Google Maps in 2018, of Zhengzhou city, the present study estimated the low-carbon level of three kinds of behavior: daily energy use at home, daily travel, and daily consumption. The study investigated the influence factors on low-carbon behavior using the hierarchical linear model. We found that residential self-selection impacts both energy use and daily travel. Residents in some built environments consumed less energy at home and contributed less CO2 emissions through daily travel than others. In particular, individual-level variables significantly affected the low-carbon energy use behavior. The female, elderly, highly educated, married, and working-class residents with children had higher levels of low-carbon energy use. Community-level variables significantly affected the level of low-carbon travel and low-carbon consumption. If residents lived in areas with high density, mixed land use, and high accessibility, their travel mode and consumption behavior would entail low carbon emissions. There is a relationship between individual variables and community variables. Different individual attributes living in the same built environment have different impacts on low-carbon behaviors.

Propensity Score Weighting with Generalized Boosted Models to Explore the Effects of the Built Environment and Residential Self-Selection on Travel Behavior

2019 ◽  
Vol 2673 (4) ◽  
pp. 373-383 ◽  
Author(s):  
Yiling Deng ◽  
Yadan Yan
Keyword(s):  
Propensity Score ◽  
Built Environment ◽  
Hierarchical Clustering ◽  
Travel Behavior ◽  
Public Transit ◽  
Average Treatment Effect ◽  
Propensity Score Weighting ◽  
Self Selection ◽  
Environment Variables ◽  
Trip Frequency

Many studies have examined the association between the built environment, residential self-selection, and travel behavior. However, few studies have quantified the relative contribution of the built environment itself. Using the 2012 Nanjing Household Travel Survey data, this study applied hierarchical clustering and propensity score weighting to study the effects of the built environment and residential self-selection on travel behavior. First, residents’ household locations were classified into four built environment patterns using hierarchical clustering based on six built environment variables by loosely following the “5Ds” (i.e., density, diversity, design, destination accessibility, and distance to transit). Second, a powerful machine learning method, generalized boosted model (GBM), was employed to obtain propensity scores. Propensity score weighting, which is more effective for multiple treatments than matching or stratification, was used to control for residential self-selection. Lastly, the observed effect (OBE), the average treatment effect on the population (ATE), and the built environment proportion (BEP) were calculated for the walking trip frequency, bicycle trip frequency, public transit trip frequency, and vehicle kilometers traveled (VKT) of six pairs of built environment patterns. The results show that a high-density, mixed-use, walkable, and transit-accessible built environment is associated with more walking trips and lower VKT but has no impact on bicycle trips and has an inconsistent impact on public transit trips. The effects of some built environment variables on bicycle and public transit trips are tangled. The residential self-selection effect has the greatest impact on VKT (BEP: 48%–77%), followed by the walking trip frequency (BEP: 62%–74%) and the public transit frequency (BEP: 90%–107%).

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